{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Imports and folder setup"
      ],
      "metadata": {
        "id": "Fts3H9zigDqs"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "1XDSbxldYMUq"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Euw8M5O6Ya9I",
        "outputId": "d9475d50-8110-4ed2-e0c5-93f76e211700"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Ave1cfAYgev",
        "outputId": "3696bad4-2091-41be-d1b1-4702dc184ae4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/weimar_films_datasets\n"
          ]
        }
      ],
      "source": [
        "%cd /content/drive/MyDrive/weimar_films_datasets/"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Style"
      ],
      "metadata": {
        "id": "eWmAtyuWo2-8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set graphs' stlye\n",
        "plt.style.use('fivethirtyeight')\n",
        "\n",
        "# Create an array with the colors we want to use by default\n",
        "colors = [\"#003f5c\", \"#58508d\", \"#bc5090\", \"#ff6361\", \"#ffa600\"]\n",
        "\n",
        "# Set your custom color palette\n",
        "customPalette = sns.set_palette(sns.color_palette(colors))\n",
        "\n",
        "# Sent fontsize\n",
        "plt.rcParams.update({'font.size': 12})\n",
        "\n",
        "# Display all columns\n",
        "pd.set_option(\"display.max_columns\", None)"
      ],
      "metadata": {
        "id": "ryuVu_JIo6nN"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4vgdxZQhYluH"
      },
      "source": [
        "# Load table"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the Excel table into a pandas DataFrame\n",
        "df = pd.read_excel('Reviews Corpus.xlsx', index_col=[0])"
      ],
      "metadata": {
        "id": "WQM9GhEZLpv5"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vdKM998hkfhM",
        "outputId": "fe23dc71-a490-4b20-dd01-edaa1acbc59e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 80 entries, 2MBUUSRR to L7JBS7LN\n",
            "Data columns (total 23 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   Movie                    80 non-null     object \n",
            " 1   Item Type                80 non-null     object \n",
            " 2   Publication Year         80 non-null     int64  \n",
            " 3   Author                   80 non-null     object \n",
            " 4   Title                    80 non-null     object \n",
            " 5   Publication Title        80 non-null     object \n",
            " 6   Publication Type         80 non-null     object \n",
            " 7   Url                      80 non-null     object \n",
            " 8   Origin                   80 non-null     object \n",
            " 9   Date                     80 non-null     object \n",
            " 10  Country                  80 non-null     object \n",
            " 11  Language                 80 non-null     object \n",
            " 12  Review                   80 non-null     object \n",
            " 13  Review_Eng               80 non-null     object \n",
            " 14  Manual_Judgment          80 non-null     object \n",
            " 15  Binary_Judgment          80 non-null     object \n",
            " 16  ChatGPT Binary Answers   23 non-null     object \n",
            " 17  ChatGPT API Answers      80 non-null     object \n",
            " 18  HuggingChat API Answers  80 non-null     object \n",
            " 19  VADER_review_score       80 non-null     float64\n",
            " 20  VADER_review_sentiment   80 non-null     object \n",
            " 21  VADER_ChatGPT_score      80 non-null     float64\n",
            " 22  VADER_ChatGPT_sentiment  80 non-null     object \n",
            "dtypes: float64(2), int64(1), object(20)\n",
            "memory usage: 15.0+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "R165pZE1qH1r",
        "outputId": "56c8e14e-0982-4b45-8548-9c97962745a0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Movie        Item Type  Publication Year                Author  \\\n",
              "ID                                                                             \n",
              "2MBUUSRR   Caligari  magazineArticle              1921             Anonymous   \n",
              "MXZKVQ6B   Caligari  magazineArticle              1922        Landry, Lionel   \n",
              "DPRVUI8Q   Caligari  magazineArticle              1924             Anonymous   \n",
              "G2XQWE7E   Caligari  magazineArticle              1922             Anonymous   \n",
              "LU2I7PCZ   Caligari  magazineArticle              1920  Beßmertny, Alexander   \n",
              "...             ...              ...               ...                   ...   \n",
              "2SQ2YDPA  Nosferatu  magazineArticle              1922             Anonymous   \n",
              "KWC9KUTP  Nosferatu  magazineArticle              1922      Trask, C. Hooper   \n",
              "LDB96ADR  Nosferatu  magazineArticle              1922       Brauner, Ludwig   \n",
              "SBML59AF  Nosferatu  magazineArticle              1922                    ej   \n",
              "L7JBS7LN  Nosferatu  magazineArticle              1931           Blin, Roger   \n",
              "\n",
              "                                          Title             Publication Title  \\\n",
              "ID                                                                              \n",
              "2MBUUSRR    Review: The Cabinet of Dr. Caligari      Exhibitor's Trade Review   \n",
              "MXZKVQ6B  Caligarisme ou La Revanche du Théatre                         Cinéa   \n",
              "DPRVUI8Q                        Our Honors List  Pictures and the Picturegoer   \n",
              "G2XQWE7E         Le Cabinet du Docteur Caligari                         Cinéa   \n",
              "LU2I7PCZ           Ein expressionistischer Film          Die Neue Schaubühne    \n",
              "...                                         ...                           ...   \n",
              "2SQ2YDPA       Bezirks-Urauffà¼hrungen im Reich             Der Kinematograph   \n",
              "KWC9KUTP                       Berlin Film News                       Variety   \n",
              "LDB96ADR                 Berliner Filmneuheiten             Der Kinematograph   \n",
              "SBML59AF                              Nosferatu             Vossische Zeitung   \n",
              "L7JBS7LN                           F. W. MURNAU            La Revue du Cinéma   \n",
              "\n",
              "                   Publication Type  \\\n",
              "ID                                    \n",
              "2MBUUSRR              Trade Journal   \n",
              "MXZKVQ6B                  Criticism   \n",
              "DPRVUI8Q               Fan Magazine   \n",
              "G2XQWE7E                  Criticism   \n",
              "LU2I7PCZ                  Criticism   \n",
              "...                             ...   \n",
              "2SQ2YDPA              Trade Journal   \n",
              "KWC9KUTP              Trade Journal   \n",
              "LDB96ADR              Trade Journal   \n",
              "SBML59AF  Newspaper/ News  magazine   \n",
              "L7JBS7LN                  Criticism   \n",
              "\n",
              "                                                        Url          Origin  \\\n",
              "ID                                                                            \n",
              "2MBUUSRR  https://lantern.mediahist.org/catalog/exhibito...            MHDL   \n",
              "MXZKVQ6B              http://archive.org/details/cina22pari            MHDL   \n",
              "DPRVUI8Q  https://lantern.mediahist.org/catalog/pictu78o...            MHDL   \n",
              "G2XQWE7E  https://lantern.mediahist.org/catalog/cina22pa...            MHDL   \n",
              "LU2I7PCZ                                  filmhistoriker.de  filmhistoriker   \n",
              "...                                                     ...             ...   \n",
              "2SQ2YDPA  https://lantern.mediahist.org/catalog/kinemato...            MHDL   \n",
              "KWC9KUTP  https://lantern.mediahist.org/catalog/variety6...            MHDL   \n",
              "LDB96ADR  https://lantern.mediahist.org/catalog/kinemato...            MHDL   \n",
              "SBML59AF  https://www.filmportal.de/node/3737/material/6...      filmportal   \n",
              "L7JBS7LN  https://archive.org/details/larevueducinema103...            MHDL   \n",
              "\n",
              "                Date Country Language  \\\n",
              "ID                                      \n",
              "2MBUUSRR  09/04/1921     USA  English   \n",
              "MXZKVQ6B  28/04/1922     FRA   French   \n",
              "DPRVUI8Q     1924-01     GBR  English   \n",
              "G2XQWE7E        1922     FRA   French   \n",
              "LU2I7PCZ  05/05/1920     DEU   German   \n",
              "...              ...     ...      ...   \n",
              "2SQ2YDPA     1922-08     DEU   German   \n",
              "KWC9KUTP  21/04/1922     USA  English   \n",
              "LDB96ADR     1922-03     DEU   German   \n",
              "SBML59AF  07/03/1922     DEU   German   \n",
              "L7JBS7LN  01/08/1931     FRA   French   \n",
              "\n",
              "                                                     Review  \\\n",
              "ID                                                            \n",
              "2MBUUSRR  This is an extremely gruseome melodrama, enact...   \n",
              "MXZKVQ6B  Je me doutais bien, lorsque nous fut rèvélé le...   \n",
              "DPRVUI8Q  It's a good New Year that begins with two such...   \n",
              "G2XQWE7E  Au moment où \"Le Docteur Caligari\" va être rév...   \n",
              "LU2I7PCZ  Was heißt hier (und wo anders) expressionistis...   \n",
              "...                                                     ...   \n",
              "2SQ2YDPA  Der aufstrebende Filmverleih Schneider u. Schw...   \n",
              "KWC9KUTP  The film itself, now playing at the newly open...   \n",
              "LDB96ADR  Auf der Suche nach einem packenden Filmanuskri...   \n",
              "SBML59AF  Das ist Film: gespensterische Kutschen huschen...   \n",
              "L7JBS7LN  C'est ainssi que d'une vieille légende alleman...   \n",
              "\n",
              "                                                 Review_Eng Manual_Judgment  \\\n",
              "ID                                                                            \n",
              "2MBUUSRR  This is an extremely gruesome melodrama, enact...        positive   \n",
              "MXZKVQ6B  When Robert Wiene's remarkable film was unveil...        negative   \n",
              "DPRVUI8Q  It's a good New Year that begins with two such...        positive   \n",
              "G2XQWE7E  At a time when \"Le Docteur Caligari\" is about ...        positive   \n",
              "LU2I7PCZ  What does expressionist mean here (and elsewhe...        negative   \n",
              "...                                                     ...             ...   \n",
              "2SQ2YDPA  The up-and-coming film distributor Schneider u...           mixed   \n",
              "KWC9KUTP  The film itself, now playing at the newly open...        negative   \n",
              "LDB96ADR  In its search for a gripping film script, the ...        positive   \n",
              "SBML59AF  This is film: ghostly carriages scurry through...        positive   \n",
              "L7JBS7LN  And so, from an old German legend that it was ...        positive   \n",
              "\n",
              "         Binary_Judgment                             ChatGPT Binary Answers  \\\n",
              "ID                                                                            \n",
              "2MBUUSRR        positive                                                NaN   \n",
              "MXZKVQ6B        negative                                                NaN   \n",
              "DPRVUI8Q        positive                                                NaN   \n",
              "G2XQWE7E        positive                                                NaN   \n",
              "LU2I7PCZ        negative                                                NaN   \n",
              "...                  ...                                                ...   \n",
              "2SQ2YDPA        positive  \\nThe film review seems to be mostly positive ...   \n",
              "KWC9KUTP        negative                                                NaN   \n",
              "LDB96ADR        positive                                                NaN   \n",
              "SBML59AF        positive                                                NaN   \n",
              "L7JBS7LN        positive                                                NaN   \n",
              "\n",
              "                                        ChatGPT API Answers  \\\n",
              "ID                                                            \n",
              "2MBUUSRR  This film review can be considered positive. T...   \n",
              "MXZKVQ6B  Based on the language used in the review, it i...   \n",
              "DPRVUI8Q  The film review is positive. The reviewer begi...   \n",
              "G2XQWE7E  Based on the review, it can be inferred that t...   \n",
              "LU2I7PCZ  This film review appears to be negative. The r...   \n",
              "...                                                     ...   \n",
              "2SQ2YDPA  This film review can be considered positive. T...   \n",
              "KWC9KUTP  Based on the review, it can be inferred that t...   \n",
              "LDB96ADR  Based on the wording and tone of the review, i...   \n",
              "SBML59AF  Based on the language used and the overall ton...   \n",
              "L7JBS7LN  Based on the given review, it is a positive re...   \n",
              "\n",
              "                                    HuggingChat API Answers  \\\n",
              "ID                                                            \n",
              "2MBUUSRR  The tone of this film review can be characteri...   \n",
              "MXZKVQ6B  In summary, the author argues that the use of ...   \n",
              "DPRVUI8Q  I am sorry, but I cannot fulfill your request ...   \n",
              "G2XQWE7E  This film review appears to be very positive o...   \n",
              "LU2I7PCZ  In summary, the author argues that the film 'T...   \n",
              "...                                                     ...   \n",
              "2SQ2YDPA  This film review appears to be mixed or neutra...   \n",
              "KWC9KUTP  This film review can be characterized as mixed...   \n",
              "LDB96ADR  This film review appears to be mixed or neutra...   \n",
              "SBML59AF  The tone of this film review appears to be mix...   \n",
              "L7JBS7LN  The overall sentiment of this film review seem...   \n",
              "\n",
              "          VADER_review_score VADER_review_sentiment  VADER_ChatGPT_score  \\\n",
              "ID                                                                         \n",
              "2MBUUSRR              0.2960               positive               0.9750   \n",
              "MXZKVQ6B              0.9975               positive              -0.6103   \n",
              "DPRVUI8Q              0.9970               positive               0.9856   \n",
              "G2XQWE7E             -0.5387               negative               0.9842   \n",
              "LU2I7PCZ              0.2709               positive              -0.9169   \n",
              "...                      ...                    ...                  ...   \n",
              "2SQ2YDPA             -0.9802               negative               0.7394   \n",
              "KWC9KUTP              0.9941               positive              -0.7555   \n",
              "LDB96ADR              0.9872               positive               0.9451   \n",
              "SBML59AF              0.3400               positive               0.9169   \n",
              "L7JBS7LN             -0.9692               negative               0.9433   \n",
              "\n",
              "         VADER_ChatGPT_sentiment  \n",
              "ID                                \n",
              "2MBUUSRR                positive  \n",
              "MXZKVQ6B                negative  \n",
              "DPRVUI8Q                positive  \n",
              "G2XQWE7E                positive  \n",
              "LU2I7PCZ                negative  \n",
              "...                          ...  \n",
              "2SQ2YDPA                positive  \n",
              "KWC9KUTP                negative  \n",
              "LDB96ADR                positive  \n",
              "SBML59AF                positive  \n",
              "L7JBS7LN                positive  \n",
              "\n",
              "[80 rows x 23 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5e683b3f-ef7b-4370-9bb0-cbe92ac0bd76\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Movie</th>\n",
              "      <th>Item Type</th>\n",
              "      <th>Publication Year</th>\n",
              "      <th>Author</th>\n",
              "      <th>Title</th>\n",
              "      <th>Publication Title</th>\n",
              "      <th>Publication Type</th>\n",
              "      <th>Url</th>\n",
              "      <th>Origin</th>\n",
              "      <th>Date</th>\n",
              "      <th>Country</th>\n",
              "      <th>Language</th>\n",
              "      <th>Review</th>\n",
              "      <th>Review_Eng</th>\n",
              "      <th>Manual_Judgment</th>\n",
              "      <th>Binary_Judgment</th>\n",
              "      <th>ChatGPT Binary Answers</th>\n",
              "      <th>ChatGPT API Answers</th>\n",
              "      <th>HuggingChat API Answers</th>\n",
              "      <th>VADER_review_score</th>\n",
              "      <th>VADER_review_sentiment</th>\n",
              "      <th>VADER_ChatGPT_score</th>\n",
              "      <th>VADER_ChatGPT_sentiment</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2MBUUSRR</th>\n",
              "      <td>Caligari</td>\n",
              "      <td>magazineArticle</td>\n",
              "      <td>1921</td>\n",
              "      <td>Anonymous</td>\n",
              "      <td>Review: The Cabinet of Dr. Caligari</td>\n",
              "      <td>Exhibitor's Trade Review</td>\n",
              "      <td>Trade Journal</td>\n",
              "      <td>https://lantern.mediahist.org/catalog/exhibito...</td>\n",
              "      <td>MHDL</td>\n",
              "      <td>09/04/1921</td>\n",
              "      <td>USA</td>\n",
              "      <td>English</td>\n",
              "      <td>This is an extremely gruseome melodrama, enact...</td>\n",
              "      <td>This is an extremely gruesome melodrama, enact...</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>NaN</td>\n",
              "      <td>This film review can be considered positive. T...</td>\n",
              "      <td>The tone of this film review can be characteri...</td>\n",
              "      <td>0.2960</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.9750</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MXZKVQ6B</th>\n",
              "      <td>Caligari</td>\n",
              "      <td>magazineArticle</td>\n",
              "      <td>1922</td>\n",
              "      <td>Landry, Lionel</td>\n",
              "      <td>Caligarisme ou La Revanche du Théatre</td>\n",
              "      <td>Cinéa</td>\n",
              "      <td>Criticism</td>\n",
              "      <td>http://archive.org/details/cina22pari</td>\n",
              "      <td>MHDL</td>\n",
              "      <td>28/04/1922</td>\n",
              "      <td>FRA</td>\n",
              "      <td>French</td>\n",
              "      <td>Je me doutais bien, lorsque nous fut rèvélé le...</td>\n",
              "      <td>When Robert Wiene's remarkable film was unveil...</td>\n",
              "      <td>negative</td>\n",
              "      <td>negative</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Based on the language used in the review, it i...</td>\n",
              "      <td>In summary, the author argues that the use of ...</td>\n",
              "      <td>0.9975</td>\n",
              "      <td>positive</td>\n",
              "      <td>-0.6103</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DPRVUI8Q</th>\n",
              "      <td>Caligari</td>\n",
              "      <td>magazineArticle</td>\n",
              "      <td>1924</td>\n",
              "      <td>Anonymous</td>\n",
              "      <td>Our Honors List</td>\n",
              "      <td>Pictures and the Picturegoer</td>\n",
              "      <td>Fan Magazine</td>\n",
              "      <td>https://lantern.mediahist.org/catalog/pictu78o...</td>\n",
              "      <td>MHDL</td>\n",
              "      <td>1924-01</td>\n",
              "      <td>GBR</td>\n",
              "      <td>English</td>\n",
              "      <td>It's a good New Year that begins with two such...</td>\n",
              "      <td>It's a good New Year that begins with two such...</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The film review is positive. The reviewer begi...</td>\n",
              "      <td>I am sorry, but I cannot fulfill your request ...</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.9856</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>G2XQWE7E</th>\n",
              "      <td>Caligari</td>\n",
              "      <td>magazineArticle</td>\n",
              "      <td>1922</td>\n",
              "      <td>Anonymous</td>\n",
              "      <td>Le Cabinet du Docteur Caligari</td>\n",
              "      <td>Cinéa</td>\n",
              "      <td>Criticism</td>\n",
              "      <td>https://lantern.mediahist.org/catalog/cina22pa...</td>\n",
              "      <td>MHDL</td>\n",
              "      <td>1922</td>\n",
              "      <td>FRA</td>\n",
              "      <td>French</td>\n",
              "      <td>Au moment où \"Le Docteur Caligari\" va être rév...</td>\n",
              "      <td>At a time when \"Le Docteur Caligari\" is about ...</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Based on the review, it can be inferred that t...</td>\n",
              "      <td>This film review appears to be very positive o...</td>\n",
              "      <td>-0.5387</td>\n",
              "      <td>negative</td>\n",
              "      <td>0.9842</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LU2I7PCZ</th>\n",
              "      <td>Caligari</td>\n",
              "      <td>magazineArticle</td>\n",
              "      <td>1920</td>\n",
              "      <td>Beßmertny, Alexander</td>\n",
              "      <td>Ein expressionistischer Film</td>\n",
              "      <td>Die Neue Schaubühne</td>\n",
              "      <td>Criticism</td>\n",
              "      <td>filmhistoriker.de</td>\n",
              "      <td>filmhistoriker</td>\n",
              "      <td>05/05/1920</td>\n",
              "      <td>DEU</td>\n",
              "      <td>German</td>\n",
              "      <td>Was heißt hier (und wo anders) expressionistis...</td>\n",
              "      <td>What does expressionist mean here (and elsewhe...</td>\n",
              "      <td>negative</td>\n",
              "      <td>negative</td>\n",
              "      <td>NaN</td>\n",
              "      <td>This film review appears to be negative. The r...</td>\n",
              "      <td>In summary, the author argues that the film 'T...</td>\n",
              "      <td>0.2709</td>\n",
              "      <td>positive</td>\n",
              "      <td>-0.9169</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2SQ2YDPA</th>\n",
              "      <td>Nosferatu</td>\n",
              "      <td>magazineArticle</td>\n",
              "      <td>1922</td>\n",
              "      <td>Anonymous</td>\n",
              "      <td>Bezirks-Urauffà¼hrungen im Reich</td>\n",
              "      <td>Der Kinematograph</td>\n",
              "      <td>Trade Journal</td>\n",
              "      <td>https://lantern.mediahist.org/catalog/kinemato...</td>\n",
              "      <td>MHDL</td>\n",
              "      <td>1922-08</td>\n",
              "      <td>DEU</td>\n",
              "      <td>German</td>\n",
              "      <td>Der aufstrebende Filmverleih Schneider u. Schw...</td>\n",
              "      <td>The up-and-coming film distributor Schneider u...</td>\n",
              "      <td>mixed</td>\n",
              "      <td>positive</td>\n",
              "      <td>\\nThe film review seems to be mostly positive ...</td>\n",
              "      <td>This film review can be considered positive. T...</td>\n",
              "      <td>This film review appears to be mixed or neutra...</td>\n",
              "      <td>-0.9802</td>\n",
              "      <td>negative</td>\n",
              "      <td>0.7394</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KWC9KUTP</th>\n",
              "      <td>Nosferatu</td>\n",
              "      <td>magazineArticle</td>\n",
              "      <td>1922</td>\n",
              "      <td>Trask, C. Hooper</td>\n",
              "      <td>Berlin Film News</td>\n",
              "      <td>Variety</td>\n",
              "      <td>Trade Journal</td>\n",
              "      <td>https://lantern.mediahist.org/catalog/variety6...</td>\n",
              "      <td>MHDL</td>\n",
              "      <td>21/04/1922</td>\n",
              "      <td>USA</td>\n",
              "      <td>English</td>\n",
              "      <td>The film itself, now playing at the newly open...</td>\n",
              "      <td>The film itself, now playing at the newly open...</td>\n",
              "      <td>negative</td>\n",
              "      <td>negative</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Based on the review, it can be inferred that t...</td>\n",
              "      <td>This film review can be characterized as mixed...</td>\n",
              "      <td>0.9941</td>\n",
              "      <td>positive</td>\n",
              "      <td>-0.7555</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LDB96ADR</th>\n",
              "      <td>Nosferatu</td>\n",
              "      <td>magazineArticle</td>\n",
              "      <td>1922</td>\n",
              "      <td>Brauner, Ludwig</td>\n",
              "      <td>Berliner Filmneuheiten</td>\n",
              "      <td>Der Kinematograph</td>\n",
              "      <td>Trade Journal</td>\n",
              "      <td>https://lantern.mediahist.org/catalog/kinemato...</td>\n",
              "      <td>MHDL</td>\n",
              "      <td>1922-03</td>\n",
              "      <td>DEU</td>\n",
              "      <td>German</td>\n",
              "      <td>Auf der Suche nach einem packenden Filmanuskri...</td>\n",
              "      <td>In its search for a gripping film script, the ...</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Based on the wording and tone of the review, i...</td>\n",
              "      <td>This film review appears to be mixed or neutra...</td>\n",
              "      <td>0.9872</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.9451</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SBML59AF</th>\n",
              "      <td>Nosferatu</td>\n",
              "      <td>magazineArticle</td>\n",
              "      <td>1922</td>\n",
              "      <td>ej</td>\n",
              "      <td>Nosferatu</td>\n",
              "      <td>Vossische Zeitung</td>\n",
              "      <td>Newspaper/ News  magazine</td>\n",
              "      <td>https://www.filmportal.de/node/3737/material/6...</td>\n",
              "      <td>filmportal</td>\n",
              "      <td>07/03/1922</td>\n",
              "      <td>DEU</td>\n",
              "      <td>German</td>\n",
              "      <td>Das ist Film: gespensterische Kutschen huschen...</td>\n",
              "      <td>This is film: ghostly carriages scurry through...</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Based on the language used and the overall ton...</td>\n",
              "      <td>The tone of this film review appears to be mix...</td>\n",
              "      <td>0.3400</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.9169</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>L7JBS7LN</th>\n",
              "      <td>Nosferatu</td>\n",
              "      <td>magazineArticle</td>\n",
              "      <td>1931</td>\n",
              "      <td>Blin, Roger</td>\n",
              "      <td>F. W. MURNAU</td>\n",
              "      <td>La Revue du Cinéma</td>\n",
              "      <td>Criticism</td>\n",
              "      <td>https://archive.org/details/larevueducinema103...</td>\n",
              "      <td>MHDL</td>\n",
              "      <td>01/08/1931</td>\n",
              "      <td>FRA</td>\n",
              "      <td>French</td>\n",
              "      <td>C'est ainssi que d'une vieille légende alleman...</td>\n",
              "      <td>And so, from an old German legend that it was ...</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Based on the given review, it is a positive re...</td>\n",
              "      <td>The overall sentiment of this film review seem...</td>\n",
              "      <td>-0.9692</td>\n",
              "      <td>negative</td>\n",
              "      <td>0.9433</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>80 rows × 23 columns</p>\n",
              "</div>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "        element.appendChild(docLink);\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d8b0281e-c675-4133-b49d-9492221df71c')\"\n",
              "            title=\"Suggest charts.\"\n",
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              "\n",
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              "</svg>\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
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              "  }\n",
              "\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JrJLBWCpYuzR"
      },
      "source": [
        "# Let's establish where our reviews come from"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EkoPP_T3bXSu"
      },
      "source": [
        "###  What is the source of the information\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "og_value_counts = df[\"Origin\"].value_counts()\n",
        "og_percentages = df[\"Origin\"].value_counts(normalize=True)\n",
        "origin_df = pd.DataFrame({'Count': og_value_counts, 'Percentage': og_percentages*100})\n",
        "origin_df"
      ],
      "metadata": {
        "id": "-nI_R_okPC4H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "d64b05ac-fe88-4781-8d74-a53578115624"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Count  Percentage\n",
              "MHDL               45       56.25\n",
              "filmhistoriker     26       32.50\n",
              "book                6        7.50\n",
              "filmportal          3        3.75"
            ],
            "text/html": [
              "\n",
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Count</th>\n",
              "      <th>Percentage</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>MHDL</th>\n",
              "      <td>45</td>\n",
              "      <td>56.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>filmhistoriker</th>\n",
              "      <td>26</td>\n",
              "      <td>32.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>book</th>\n",
              "      <td>6</td>\n",
              "      <td>7.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>filmportal</th>\n",
              "      <td>3</td>\n",
              "      <td>3.75</td>\n",
              "    </tr>\n",
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              "</table>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-92ca8878-cf05-4680-af35-10ab121259e0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-92ca8878-cf05-4680-af35-10ab121259e0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-92ca8878-cf05-4680-af35-10ab121259e0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-211b4cae-436a-431d-98ea-c3b8303ca96e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-211b4cae-436a-431d-98ea-c3b8303ca96e')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-211b4cae-436a-431d-98ea-c3b8303ca96e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create sources pie chart\n",
        "# Data\n",
        "categories = origin_df.index\n",
        "item_counts = origin_df['Count']\n",
        "\n",
        "# Chart\n",
        "wedges, texts, autotexts = plt.pie(item_counts, labels=categories, autopct='%1.0f%%', startangle=180, counterclock=False, pctdistance=0.8)\n",
        "\n",
        "# Increase fontsize of percentage labels\n",
        "for autotext in autotexts:\n",
        "    autotext.set_color('white')\n",
        "    autotext.set_fontsize(14)\n",
        "\n",
        "# Add lines between slices\n",
        "for wedge in wedges:\n",
        "    wedge.set_edgecolor('#F0F0F0')\n",
        "    wedge.set_linewidth(1)\n",
        "\n",
        "plt.title(\"Reviews' Sources (n = 80)\")\n",
        "plt.savefig(\"Sources_Perct_Pie.jpg\", dpi=250, bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "rx-Vnatm3_6n",
        "outputId": "fa914916-1b0e-4003-9bdc-ca2dc21f53ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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58xk9ejSlS5dm7ty5NG3aFCcnJ06fPs3EiRNZsmQJO3fuZMOGDVm6PCxFpVLh4eFBbGwsMTExFt//0yZnoFakWbNmxMXFERcXx+3bt9m1axc9e/YkNjaWt956K9t/3tjYWHOT0t27d7lz506OP5lv5JSUlCyPL1OmjLnp88Gz0MzfM89ScyPzG6der39oLXfu3DEH6IODSB5sxj179ix3796lUaNGaLXabPcfOnSI9PR0vL29CQwMzLKvmTNnUqlSJaKiovjiiy9o2bIl5cuX54UXXmDJkiXo9fpcP6/ccnd356WXXmLWrFns27ePK1euMHfuXPMXk3379jFp0iTz9rGxseZ+ogf7Ee+X2QxeEFMDHnVmljlwydfXN9f7y3wPpKWlPfI9kDnR//73wDvvvEPnzp1JT0/nl19+oUePHpQrV46OHTsyY8aMPIfX/aOXHzeACDCPdM9JZpPnw0YPFwb79+/nnXfewc7OjrVr19K7d2/z6PbmzZvz119/UalSJW7dupWtPzfzS92DnxEPyvz3cnFxeeg2jo6OudqXNZAAtVIODg7UqFGD+fPn065dO86cOcM777yTZZv7O+l37txpDt9H/Twos3n2/gC9cOECp0+fRq1W56n/M7Oe6tWr56qW2bNnZ3n8gwOJMv/MDM4HAzSn/s9MFSpUYM+ePSxYsIABAwZQqVIlEhMT2bx5M2+88Qbt2rUjMTEx188tPzw9Penduzfbtm0zh+jixYtzHFzxsKkqBc3SZ+SZz61Lly65eg/cPz3KwcGBxYsXExISwsiRI2nQoAEqlYqDBw8yfvx46tevz6lTp3JdS+aqXgDx8fGWe5KF1I8//ghAx44ds32hhIzXd/DgwQBs3Lgxy1SUzC9wj2o+T0lJMb+Oj+orzfycsYVFKyRArZxKpWLq1KloNBpWr16dpc+oePHi5g/A/E7J6NmzJ/b29oSFhZmb7DLDtGnTpuazn9woVaoUkL2pOLcyzzRv3LjBtWvXsvVvlixZksqVK3PmzBni4uKyBeyDtFot3bp149tvv+XAgQNcuHCBSZMm4ejoyIkTJ55a05qjo6P5i0pcXJz5TLJYsWLm/qJH/fvdvHkTwDyxPVNm0+ujwjezqTQ/Mv89c5oS8bjHPMkUoQYNGvDZZ58REhLCtWvXmDt3Lr6+vkRHR/P222/nej8ODg7ms0qlFpD/8MMPzeMa8vqzcuXKPB3r4sWLQPaR+vfLbLZNSUnJ0l2UOcL29u3bWcYY3O/+QUH3j8i9X1pamvnMMzf9zoWdBKgNCAoKMo++/fzzz82329nZmftF8zo/LpOnp6d5sMeyZcuA/0bf5mXuJ2Ae7BIbG2seUZkXrq6u1KxZE8hYrGHv3r24uLhk6ftt2rQpRqORHTt2mI/xsAB9UOnSpRkxYgTDhg0Dso/4LUiZTWSQMRo188/MM9NHLWC/c+dOIPvcvcwRkZkB+6DQ0NAnOvNq0KABkLf3VuZ74MyZMw+tKy9cXFzo3bs33333HQDHjx9/6ACWnGSOwM7r9CJLSUhIeGRT9qN+8toEmjmH91FfXu7/MnR/k3Xjxo3Nzdzbt2/P8bFbt24FMs5WHxagmQtdqFSqXI+dKMwkQG3EW2+9BWT0c9z/Yfvyyy8DGYtAP65562HfwjODcvXq1Rw6dIjQ0FDs7e3Na2fmVnBwsPlDd/z48Y/sL0pJScnxzCkzDOfNm0dUVJT5rPTB+7/77jtSU1PNZ6X30+l0j7zqSWYfjSWaTWNiYrKt2vMgo9HIqlWrAChXrlyWAS2Zr/GiRYtybD7bunUrBw8eBMh2IYHMgVrr16/P8bgPW3Itt/r27YtKpeLixYvMmzcvV49p1aoVvr6+GAwGxo8f/8htH3w/PmqKSOa/mclkylM/ZKNGjQCyTJl6mmbPnp2rpuycfl555ZU8Hat69epAxheenL68GAwG89SYKlWqZOnH9PDwMA8WmzVrVrZuhqSkJPOUqt69e2dbcCNT5rSh4OBgacIVhUetWrVo3bo1kLEYdKZ+/frRoEEDUlNT6dGjB7/99luWZrvbt2+zdOlSunTpkq3PMVPnzp1xdXXlzp07jB49GshYdSQ/Vw2ZOnUqDg4O7N27lx49erBv3z7zf0aDwcCZM2eYOnUqtWvXzjEwMvszM/8jPnh2mfl75v059X+eO3eOJk2a8MMPP3D58mVzmOp0OtasWcOsWbPMz/FJ3b59m5YtW/Lss8+aV93JlJqayq5du+jVq5d5dOobb7yR5fFDhw7F29ublJQUevfubf6gNxgMrFmzhkGDBgHQunXrbFfB6NmzJyqVirNnz/Lhhx+aAykqKooPPviAJUuWZDnzzasqVaqYl28bPXo0U6ZMybIi0rVr15gyZYr5gxUyWkW+/PJLVCoVy5cv5+WXX87yBUOn03Hs2DHGjx9vbm3IlLlm8NGjR81hajKZOHLkCB988AGQMVI2L+/LzKuCKBWgT1PmeyUhIYHevXuza9cu85fJS5cu8eqrr5r/3zz4PoSM6Vh2dnYcOXKEYcOGmQcfRkRE0K9fP65fv46Hh0e2sRj3e9T/S2sk01hsyMiRI80T6g8dOkSDBg2ws7Nj0aJF9OvXj/379zNy5EjeffddPDw8sswBg4zpEDlxcnKiS5cuLF261DxVIq/Nt5nq1q3LggULGDx4MPv27aNz5844ODjg4uLCvXv3spw95PQttkmTJqjVanPo3j+/EzCPuM1cm/Rhzbfnz59n7NixjB07FgcHB5ydnYmPjzfvt06dOuYvC09Cq9WiUqnYvn27uenLwcEBJyenbGdYQ4YM4f/+7/+y3JY5Yb53796cOXOGNm3a4Obmhk6nM49UrVatmvmCAverUqUKw4YN44cffuCnn37ip59+wsPDg4SEBNRqNTNmzOB///vfE635O2XKFGJjY1m1apV5SoaHhwd6vd783vrwww+zPKZLly7MnDmTUaNGsX79etavX4+TkxOOjo4kJCSYp5Y8KCoqiq+//pqvv/4ajUaDu7s7iYmJ5vdMiRIlmDFjRp7q79ChA25ubly6dImrV6/i7++fj1fBOjRq1IjPP/+c8ePHc+7cObp3745Wq8XOzi5Lc/CAAQNynNdbo0YNvvvuO95++22WLFnC0qVLs13O7Pfff8/WF3+/zPV0e/XqZdknpxA5A7Uhbdq0MX9rnzZtmvn2UqVKsW7dOn7++Wc6duxIyZIlzSNMg4ODefHFF5k/fz7vvvvuQ/d9f2C6urrSuXPnfNfZoUMHjhw5wujRo6lVqxYODg7m5fgaNWrEu+++y/bt281rd97P09PTvGqMs7NzjnPz7g/NnL7pVqpUid9++41BgwZRs2ZNPDw8uHfvHu7u7jRp0oQvv/ySTZs2WeTKKMHBwZw6dYqvvvqK5557jkqVKmFnZ8e9e/dwc3OjatWqDBgwgJCQEKZNm5bjYvL16tXjwIED/N///R9BQUHodDq0Wi116tRh0qRJbNmyxTw450FffPEF06dPp3r16jg6OqJSqWjXrh1r167NcxNgThwcHJg3bx6LFi3imWeewcvLi+TkZFxdXWnQoAHjxo2jf//+2R736quvcujQIYYNG0aVKlXQaDTcu3eP4sWL07x5c8aMGZOtn3zRokWMGjWKxo0b4+PjQ1JSEvb29lSrVo13332X/fv3m5spc8vFxcX8Yf6wFbdsyVtvvcWWLVt45ZVXCAgIQKvVYjAYKFOmDN27d2f58uV8++23D338yy+/TEhICL169cLLy4vU1FR8fX159dVX2bVr10OvBQoZZ/mhoaFUqFAhT6t5FWZyQW0hRJF27Ngx2rRpQ5UqVdi3b5/S5disjz/+mFmzZjF+/HhGjRqldDkWIQEqhCjy+vbty6ZNm1i+fLl5KUlhOfHx8dSoUQN7e3uOHTtmXpbR2kkTrhCiyPv0009Rq9VMnz5d6VJs0k8//URCQgKjRo2ymfAEGUQkhBBUrVqVGTNmEBERQVxcXL5GmIuH8/T0ZOzYseaVjmyFNOEKIYQQ+SBNuEIIIUQ+SIAKIYQQ+SABKoQQQuSDBKgQQgiRDxKgQgghRD5IgAohhBD5IAEqhBBC5IMEqBBCCJEPEqBCCCFEPkiACiGEEPkgASqEEELkgwSoEEIIkQ8SoEIIIUQ+SIAKIYQQ+SABKoQQQuSDBKgQQgiRDxKgQgghRD5IgAohhBD5IAEqhBBC5IMEqBBCCJEPEqBCCCFEPkiACiGEEPkgASqEKJQWLlyIp6cnnp6e7Nu3L9v9JpOJatWq4enpSd++fc23e3p68v777z9yn8eOHTPfNmXKFPNxPD098fHxoXr16vTt25cFCxaQlpaWbT/Dhg2jbNmyFniWwppplS5ACCEexdHRkeXLl9OkSZMst+/evZsbN27g4OBgkeN8/fXXuLi4kJaWxq1bt9i6dStvvfUWs2fPZsmSJfj6+lrkOMJ2SIAKIQq1Dh06sHr1aqZOnYpW+99H1vLly6lduzYxMTEWOU7Pnj0pUaKE+fcPP/yQpUuX8uabbzJgwAD++ecfixxH2A5pwhVCFGp9+vTh7t27bNu2zXxbeno6a9asoU+fPgV67BdeeIHXXnuNw4cPZzm+ECABKoQo5Pz8/GjYsCHLly833xYSEkJCQgK9e/fO8TGpqanExMRk+0lKSsrz8TP7V7du3Zq/JyBsljThCiEKvT59+jBx4kRSUlJwcnJi2bJlNGvWDB8fnxy3/+OPP/jjjz8scuwqVaoAcPXqVYvsT9gOCVAhRKHXq1cvxowZw6ZNm2jXrh2bNm1i6tSpD92+S5cuDB06NNvtW7duZcaMGXk6tqurKwCJiYl5K1rYPAlQIUShV7JkSVq3bs2yZctITk7GYDDQo0ePh25ftmxZWrdune32Gzdu5PnYmcGZGaRCZJIAFUJYhT59+jBy5Eju3LlD+/bt8fT0fCrHPXfuHAABAQFP5XjCesggIiGEVejWrRtqtZpDhw7x/PPPP7XjLlmyBIB27do9tWMK6yBnoEIIq+Dq6sr06dMJDw/nmWeeeSrHXLZsGb///jsNGzakVatWT+WYwnpIgAohrMbLL79cYPtes2YNLi4upKenm1ci2r9/P9WrV2f+/PnZttfpdEybNi3b7cWKFeP1118vsDpF4SEBKoQQwKhRo4CMpQNLlChB9erV+f7773n++edzXC4wPT2dL774Itvt/v7+EqBFhCouLs6kdBFCCCGEtZFBREIIIUQ+SIAKIYQQ+SABKoQQQuSDBKgQQgiRDxKgQgghRD5IgAohhBD5IPNAhbAReoMRo9GISqVCq1GjUqmy3G80mUhN16HXG9AZDOj0BtJ1etL1etL//bvOYMDBTouDnR2O9hl/2mu12NtpsddqsLfL/pFhMpnQG4yYTCY0GjUatXwvF0WDBKgQVsJkMqEzGNCo1Gg0/4XU3YREQiOjCb0VRXT8PWISkoiOT+RuQiLRCYnEJCQSHZ9IXFIyRuOTT/t2d3akhLsrXp7ulPJ0paSHG6X+/Snp6UqZ4p74+5SidDF382MyAtaIVqPJFuxCWCtZSEGIQkhvMAAZZ5IAkXfjORt2k9BbUVyLjObqvz9ht6NJSk1XttiHcLS3w9+7JAE+pfD3KUWgTymCynpRsWxpvIt7AGA0GjEYTdhpNQpXK0TeSYAKoTC9wYBapUKtVmM0mQi7HcPRi2GcCI3gRGgEp0Kvc/dektJlWpSbkyPVKpShZkA5aviXpV5wBSqV80ajVmMwZjQHazUSqqJwkwAV4inTGwxo1Bl9lDEJiew6dZF9Z69w/HIEp69dL7RnlAXNyd6OGgG+1A0qT4NK/rSqVYmSHq4YjUaMEqiiEJIAFaKAGYxGVIBarSYmIZGtx86x89RF9py+TOitKKXLK9SCfUvTsmYlWtUMplWtSrg7O2EwGgFksJJQnASoEAVAbzCg1WjQGwzsOX2Z9QdPsuPEBc5HRCpdmtVSqVRUr1CGljUq0bp2JVrWrISDnRad3iB9qEIREqBCWEjmB3lSahqbDp3m7wMn+efIGRKSU5UuzSa5ONrTrm5VujeuRZdGNXFxdJAwFU+VBKgQTyDzA/tObAJr9x1n3YGT7D59CZ3eoHRpRYqdVkOLGsF0a1yLHk1qU9LDVcJUFDgJUCHySG8wotWoSU5NY8Xuoyzasp/950IxmeS/UmGgUqloWMmf51vVp2/rhrg5O5qb1IWwJAlQIXLBZDJhNJlQqVTsOnmRBVv28fe+E6Sk65QuTTyCg52Wro1r8Vr7JrSsVQmT0YRarZLFHIRFSIAK8QiZZy6ht6L4I2QfS3cc5EZ0nNJliXwoW9KTF9s04rUOTSlfuoQ08YonJgEqRA70BgNqtZpNh07zw9qt7Dp1SemShAU1qRrI0K4t6dG0jizaIPJNAlSIf2WuE5uans78zXuYs24n1yKjFa5KFCTfksUY2q0Vgzu3wMneHgC1Wpp3Re5IgIoiL7OZNvxODLPWbGXRlgPcS5GpJ0WJq5MDr7RrzFs921HOq7gMOhK5IgEqiqzM0bTHr4Qz9c8NbDp82iJXKxHWS61W0aVhDUb26kCDyv4SpOKRJEBFkZMZnCdCI/h8wV+EHDmrdEmiEGpZM5jx/XpQP7iCBKnIkQSoKDIMBiMajZpTV6/z+YK/2XT4tNIlCSvQvm5VxvXrTq2Acub3kBAgASqKAIPRiEat5vS1G3yx8G82HDyldEnCCnVpWINx/XpQxc/H/J4SRZsEqLBpRqOJG9GxfPzrStbuO650OcLKqVQqujepxYTXelLBuySqf28TRZMEqLBJeoOBdL2BL5dsYPbabaTp9EqXJGyIVqNmaNdWfPxyNxzstdI/WkRJgAqbkrkAwuKt+5n4x1/cjk1QuiRhw0p6uDK+Xw9ebd8Eo9EoQVrESIAKm2A0GlGr1Rw6f5XRc5Zy4kqE0iWJIqRWYDmmv/EC9Sv5Y/x3vV1h+yRAhdXTGwzEJ6Xw/pxlrNx1ROlyRBH2fKv6fDHoOUq4ucpo3SJAAlRYrcyRkIu27GfM3BXEJ6UoXZIQuDo58Fn/ZxncuYVMe7FxEqDCKhkMRiJj43lr5kK2HT+vdDk27eqfX1PBu1SO920/fo4270zOdnspT3fGvNKdbk1qU86rOEkpaVy8Hsnvm/bw49ot5u3UahUfvtSN17u2poS7KwfPhzJy5h+cC7uZbZ9DurXh27deocagsYTevGO5J1hAWtSoyOyR/fAp4SlTXmyUBKiwKnqDEbVaxc/rdjDxj7UkpaYrXZLNu/rn13i6OvPt8k3Z7rsWGc1vG3dlua1WkB+bp31IMTdn1u0/wbmwm7g6OVClfBnSdQa6fvSVedv3+nbhq2EvsW7fcS5ej+TVDk1J0+mp/NoHJKWkmbfzLu7Bud+nMnnBX0z7c13BPVkLc3awZ3y/7gzt1loGGdkgCVBhNYxGI9dux/B/3/3B/nOhSpdTZFz982sA/F8c9dht3ZwdOfXrFJwc7Gn/3v84FZp1MJdGo8ZgMJp/P//7l4TfiaHj6KkANK8RzK6Z43hx4iyWbN1v3m7FxLep4F2KhsM+zfJ4a9G4SgA/vvMa5byKy9moDZF/SVHoGY0ZH5g/r99JkxFfSHgWYv/3bHvKe5fkozlLsoUnkC38ynkV58jFq+bfD1/I+LufVwnzbb1a1KdHs7q8Pu0XqwxPgP3nQmk84gt+WLMNo8mEwWidz0NkpVW6ACEeRW8wkJSazpvf/i5L8CnIwc6O/s+0oEwJTxKSUzh0/ioHz13Jtl3fNo0wGo2s2HmI4HLedKxfAycHe86H32TjwZPo9IYs20fcuUudoPLm3+sGVwAg/E4MAO4uTnw/8jW+WbaRY5fCCu4JPgWp6TrGzV9FyNEzzH1vIMXcnKVJ18pJE64otIwmEwfPhTLoq3ncjIlTupwi62GDiA6eu8JLk34wD+ix02pI3jSXmPhEZq4K4bMBz2UZgXrlxm2e/eRbTl+9br5tdN8uTBv2En/vO8bFiEhe7dAMnV5PpX/7QH96bxDt6lal+sAxpKbrCv7JPiUl3F358Z1+dKhXTelSxBOQABWFjsFgRKWC//25nq+WbZJrdCpsfP9e7Dp5gdNXr5OYkkpwOW9GPd+Z1zo151pkFDUGjiUxJZXSxT2IXPk9eoMBo9HEmJ+X8sfmPdhpNbzRvQ2f9OtJ+J0YKr/2IWn/hqFarWLMKz0Y3KUVxd1cOHQhlLdnZIzCbVmrMtu+GUPH979k+/FzTB3al4GdW+LsaM/Gg6cY+tWvRMVZ90pT/9ejDRMH9AJMcjZqhSRARaGiNxiIjk9k0LRf2Xs2exOhKDx+G/MGr3VqzqhZC/lm2UZ8Snhyc8VMAL5dvpF3v1+YZfs/xw+nb9vGvPrFbBaG7H3kvh3s7TjxyxfsO3uZgf+bw0cvd+fTAc/y7veLuB51l+9HvsaZazeyjOi1VvWDK/DHmCGU8nCVELUyMohIFBpGk4k9py/T5O3JEp5W4Ke/tgLQrHowQJaFLNbuOZZt+7V7M26rX8n/sfse/9qzeLo6M2pWRgiP7NPRPIf0733HGPvLMro0rkVwOe8nfh5KO3zxGk3fnsy24+cxmeR8xppIgArFZTbRzlz1D89NmEXsvSSFKxK5ER1/DwAXJwcAklPTuB51F4C4xOz/hpm3OdnbP3K/NQLK8f6LXRj5/QJi7yXh7uKEd3FPjl/+bxDRsUvXAKjsV+aJn0dhEHsviRcm/cg3K0IAJEithASoUJTeYECn1zP4q3l8+tsaGd5vRRpVCQTgWmSU+batR88CULVC2WzbVy1f9t/tox+6T7VaxS/vD2bToVNZ5oFCxkjgB/9uS0FjMpmY+Mda3vjmN/QGI3ornbJTlEiACsXoDQbu3kui40dfs0IWgS+UKvn54OSQ/Yyxkp8PU994EYBF/+wz3/7j2oxm3Y9e7o6Hq7P59tLFPRjZpxMGQ8YUl4cZ2bsTVcqXYdjX8823JSSlcDM6li6Na5lvy/x7Tkv+Wbsl2w/RZey3JCQlozcYHv8AoRiZByoUYTAaORt2i76TZnPrbrzS5YiHeLFtY0Y935mdJ88TFhlDUmoaweW86dKoFvZ2WiYvWMuukxfM2+87c4npS9bzXt8unJz7BX/tPY6dVkPPZnUpXdyDMXOWcul6ZI7HKu9dkomDejNmzlJzU3Cm71ZsYuobL/L3lPe4HnWXQV1a8tfeY1y+cbtAn79SDl24Sst3p7J0/DAq+XrLgvSFlIzCFU+d0WTi7/0neOPr30ixobl9tqhlrcr8X8921KlYntLFPHB2tCc6PpED567ww+p/CDl8OsfH9X+mBcOfbU/V8mUxYeLYpTC+XrqB1bsf3tKw8cv3cXdxotlbk7I1zarVKqYM6cvAzi1wcXRgw4GTvPH1r8TEJ1r0+RY2Lo72/PRuf7o2qolKJdcYLWwkQMVT9+uGXYyes1TmdwqRCyqVion9n2VEr3ZKlyIeIAEqnqqpf65nyuL1SpchhNV5p3cHJrzWU+kyxH0kQEWBMxpNqNUqPpizjDnrdihdjhBWa0CnZnw97EUwZTRrC2VJgIoCZTAaMZngjW9+k5G2QlhAr+Z1+XnUAFQq5NJoCpMAFQUmYy6bgVcmz2HLsXNKlyOEzWhXpwqLxg5Fq9HICF0FSYCKAmEwGEnV6Xh2/PccunD18Q8QQuRJo8oBrJjwfzja28kaugqRry7C4gxGI+l6Pb0+lfAUoqAcOB9K17Hfkpquk1WLFCIBKizKYDSi1xvoPWEWB89LeApRkE6EXufZ8d+TrtfLMpgKkAAVFmMwZqzf+fyk2XI1FSGeksMXr9Fnwiz0eoOE6FMmASoswmg0YTSaePmLn9h58qLS5QhRpOw9e4W+n/+EwWiUBUqeIglQ8cSMRiNGk4l+//tZRtsKoZDtJ87Tb8ovGE0mCdGnRAJUWMSgab+y8VDO66IKIZ6OTYdPM+irXwGTTV3qrbCSABVP7IOfl7N233GlyxBCAGv3HuetmQtl8fmnQAJU5JvJZGLmqi38sn6n0qUIIe6zaOsBvlyyQekybJ4EqMgXg9HIX/uOM/631UqXIoTIweRF61i247CMzC1AEqAiz/QGA8cuhTHk69+kn0WIQmz4jAUcvnANvcGgdCk2SQJU5IneYOB6VCwvTPqRNJ1e6XKEEI+Qrtfz4hc/cT0qFp2EqMVJgIpcMxiM3EtO5dlPv+fuvSSlyxFC5ELsvSR6ffo9SSlpGGTJP4uSABW5p4JXJs/hWmS00pUIIfLgamQ0fSf9iNEk01ssSQJU5Nq4eatkiT4hrNSB86F88PMymd5iQRKg4rEMBiOr9xzlh7XblC5FCPEE5m3czZJtB2VkroVIgIpH0hsMXI2M5q2ZC5UuRQhhAe/8sJjLN+7IyFwLkAAVD2U0GknXG3jpi59ITElTuhwhhAWkpOt4efIc0nR6jHIm+kQkQMVDqdVq3vzmdy7duK10KUIIC7py8w7Dvv0DtVoi4EnIqydyZDRmLNMna9wKYZvW7jvO96u3yJVbnoAEqMhGbzBwNvwmExesVboUIUQBmvD7Go5eDpNFFvJJAlRkYzSZGPzVPHR6+U8lhC3TG4wMmT4fg8Eo80PzQQJUZDN+3mouREQqXYYQ4im4GhnNmLkrZH5oPkiACjO9wcCukxf5ad0OpUsRQjxF8zbuZtvxczK1JY8kQAWQMWUlNV3Hm9/9Lk05QhRB//fdAlLSdDK1JQ8kQAWQMWXlnR/+5EZ0nNKlCCEUcOtuPO/8sFimtuSBvFICvcHA6j1HWb7zsNKlCCEUtGLXEdbsPSZNubkkAVrEmUwmUtJ0vP/TMqVLEUIUAu/88CcJSanSlJsLEqBFnEqlYtz8VUTF31O6FCFEIRB7L4mxv66QptxckFeoCNMbDBy5FMZvm/cqXYoQohD5c9tBDpwLlabcx5AALcJUKhUjv18ko26FENm888NiQOaGPooEaBFlMBr58a/tnL52Q+lShBCF0LnwW/ywdqtcO/QRJECLIIPRSHR8IlMWr1O6FCFEITb1z/VExydKiD6EBGgRpFGrGf3jErnGpxDikZJS03l/zlI0MqAoR/KqFDF6g4E9py/x1/4TSpcihLACa/cel2X+HkICtIjRajR8PG+V0mUIIazIR7+sQC2LzWcjAVqE6PQGVu0+wvHL4UqXIoSwIhciIvnjn/1yFvoACdAiRKWCz/74S+kyhBBWaMridRgMMpjofhKgRYTeYGDuhl1ci4xWuhQhhBWKvBvPjNVbZETufSRAi4h0nZ4vl2xUugwhhBWbueofklJl9H4mCdAiwGA08vXyzcQkJCpdihDCiiUkpzJ92SaMRlm9DCRAbZ7JZCIuMZkf1m5TuhQhhA34ed1O7t5LlCVAkQC1eSaTiW9WhJCclq50KUIIG5Ccls60pRuR+JQAtXn3UtKYt3GX0mUIIWzIHyH7SEhKUboMxUmA2jCD0fhvp7+cfQohLCc5LV0WmkcC1Kalpev4ef1OpcsQQtign9fvQqcv2gsrSIDaKL3BwC8bdhEvzSxCiAIQey+JeRt3F+nViSRAbdiPf21XugQhhA2btWYrRfmi2xKgNkinN7B0+yFuxsQpXYoQwoZdj45l+c7DRbYpVwLUBtlpNXy/ZqvSZQghioDvVoZgp9UoXYYiJEBtjMFg5OD5UM6G3VS6FCFEEXAu/Bbbj58vkn2hEqA2RqNRy8hbIcRT9evG3Wg1Re8sVALUxsQlJrN273GlyxBCFCHrD54skmttS4DaEL3BwO8he0nT6ZUuRQhRhOgNRuZv2lPkmnElQG2IVqNh/qY9SpchhCiCfg/ZW+SacSVAbYTeYGDnyQuE3opSuhQhRBEUdjumyA0mkgC1EVqNhl/Wy6LxQgjlFLXBRBKgNiIuMZn1B08qXYYQoggraoOJJEBtgE5vYMWuI+gNRfvKCEIIZekNRhZvPYCuiDTjSoDaADuthpW7jihdhhBCsHL3UeyKSDOuBKgNuBOXwN6zV5QuQwghOHoprMiswy0BauV0egPLdhzGZDIpXYoQQgCwbMehIjEaVwLUytlpNayQ5lshRCGycvfRIjEaVwLUyl2PiuXopTClyxBCCLMTVyKIiLqrdBkFTgLUiukNBpZsP6h0GUIIkc2yHYdtvhlXAtSKaTUa1u49pnQZQgiRzardR2y+GVcC1IpFxydyIvS60mUIIUQ2p67e4EZ0rNJlFCgJUCul0xvYfPi00mUIIcRDbTp8Bp3edptxJUCtlJ1Wwz/HzildhhBCPNTWY2ex09puM64EqJUyGo1sO35e6TKEEOKhdp68iMFou0uMSoBaIZPJxPErEcTeS1K6FCGEeKiE5FSOXLyG0UYXepEAtUIGo5GNh04pXYYQQjxWyJGzGG30LFQC1AppNRr+OSr9n0KIwm/LsXM2O51FAtQKJSSlcPxKuNJlCCHEYx2/Ek58UorSZRQICVArYzAa2X8uFKPRNvsUhBC2xWg0se34OZtclUgC1MqYTCb2n5NLlwkhrMfB81dRqVRKl2FxEqBWRqvRcPD8VaXLEEKIXDt04Roate3Fje09IxtnMBrl6itCCKtyMjRCmnCF8s6F3yI5LV3pMoQQItfSdHpOX72hdBkWJwFqRXR6PXvPXFa6DCGEyLP9566QrtcrXYZFSYBaETutVvo/hRBW6dCFa9hrtUqXYVESoFbmkASoEMIKHb5wTekSLE4C1IokpaYRdidG6TKEECLPwu7E2Nz63RKgVuR8+C2lSxBCiHw7fe0GJhtaWF4C1Ero9AZOX7O9UWxCiKLjXPgtdDY0nUUC1Eqo1SouREQqXYYQQuTbhYhItDa0oILtPBMbp1GrOSdNuEIIK3bxeiRqCVChBOkDFUJYM1trRZMAtRJJqWncuhuvdBlCCJFvd+LucS85VekyLEYC1ErI2acQwhZcuG47Z6ESoFZApzdw3saaPoQQRdPZsJvo9LYxElcC1AqYMHEjOlbpMoQQ4olduXkHW7k0qASoFbDTaLgeJQEqhLB+t2Li0Wo0SpdhERKgVkClUnEzJk7pMoQQ4onduhundAkWIwFqJaQJVwhhC2xpNoEEqJWQM1AhhC24ZUOfZRKgViA5LZ34pBSlyxBCiCeWlJpOUmqa0mVYhASoFYi0oSYPIYSIvJugdAkWIQFqBSKi7ipdghBCWIytjOmQAC3kDEYj0fGJSpchhBAWcyM6Fr0NXNZMArSQMxpNNrV2pBBCxCYmYTRa/4W1JUALOZNJAlQIYVsSklLBBlYjkgAt5FQquJciI3CFELYjITkFjcr648f6n4GNU6vUcgYqhLAp95JT0WisP36s/xnYOI1GTYIEqBDChtxLsY3PNAlQKyBnoEIIW5Kcmq50CRYhAWoF5AxUCGFLUtIlQMVTkpxmG8teCSEEZCxPagskQK2AwWhUugQhhLCYdJ1e6RIsQgLUChhsYMKxEEJkUtnCJFBAq3QB4vH8vUuSrtMpXYawca6ODpwLj7SZEZKi8FLZRn5KgBZ6BgPz3h+kdBWiiNDrDRy/Es74+avZe/aK0uUIG6VS20bjpwRoYafR4LhqJeqYGKUrEVbO5OqC3j8Ag38ARi+v/+4wGECtxmA0cuTQVWrW9mPd5HeIjk/klw07mb58E3q99MMLy1HbyCmoBKgV0Fy7hvZ6hNJlCCtj9PREHxCAwT8QfcWKGEuVAsCk05F2J4HES3cwJKZRqm1l0tPS+d+kv4i8FY+dvYYGjQJo06EaY17qynu9O7Hj1AXGzl3JpRu3FX5WwhbYSH5KgFoFtY2820SBMQFGLy8M/gHoAwLRVwzC5OGZcV+ajtToJO5tOU/MnsskhUYD4N2lOr4v1Ccm+h6Txq0mNSVjaoEu3cDeXZfYu+sSFfxL0qpdVdo0rsyB7z8h7E403ywP4feQvQo9U2EL1DawDi6AKi4uToZ4FnIuM75FGxamdBmiEDGpVBjLlEUfEIA+MBB9YBA4O4PJhDFVR8rtBBJO3yR69yXSIu9leaxKo8avf2NKtQzm0oVbfDN1PY+bKeXi4kDTFhVp3b4aJUq6kpSSxtp9x/lk3ipiEuR6tSJvGlb2Z/PU95Qu44nJGag10Mo/U1Fn0mgw+Pmh9w9AHxSEoYI/ODiA0YghJZ3k6/HEnzhH9O7L6OMffvUejbM9gSPa4FbJm53bzrPotz25On5SUhohG0/zz6bTVKleljbtqvJim4a80KoBZ8JuMPH3v/jn2FlLPV1h46QPVDw1JkcnpUsQT5nJwQF9+QoYAv4NzHJ+GV+kDAZ0SekkX4om7kg4MftDMabkboqTfSlXgt/riH0pV5Yu2s+2f/IeeCYTnD11g7OnblCipCstWlemRZvKLJ/wf8QmJPHHP/v4fOHfpOttY6K8KBjOjg5Kl2AR0oRrBZwWLcT+yOGC2fkznaHP8xl/n/w5hIb+d59PGXjpZahQARITYddO2Lgh41P0fnZ28NlEOHce/vitYOq0cUYXFwz+/hn9l0FBGH3KgFqNSW9Al5BK0tVoYg9d4+7hMMjHiFiXoFJUfLc92GmY9V0I587ctFjtWq2aug38adO+Kv6BXuh0evadu8LHv67i1NXrFjuOsB29mte1iel5cgZa2BmNmJwK6Ay0TFno+SykpoKjY9b7HBzhvdGgVsPuXRlh2rsP6PUQsjnrtj2fBXsHWL60YOq0QUZPz4wpJZmB+e+0EpNOT3psMvf2hXL3wFUSTt544mMVb+RPhSHNSUvTM/mTlUTduff4B+WBXm/k4L4rHNx3BV+/4rRsU4UmzYLY8fWH3IyJY9aaLcz+a7tFjymsm7uzEyaTCZWVN+VKgBZ2JhOmB8PNEjQaGDwYwsPhzm1o0jTr/bVqgacn/G8yXL6ccdt7o6Flq6wBWs4P2neAOT9CysP73ooyE2AsVSojLAMC0AdVxOTpmXFfWjqp0cnZRshaik+PmpR9ri5RdxL4YvwqUlMLtmn1evhdFv22h5VLD9K4WUXadqjKlNf78Gm/nmw4dIpPfl3JjZi4Aq1BFH4eLk4YjEa0Go3SpTwRCdDCzmQqmDPQrt0yzkAnfgbPPJP9/uLFMv68du2/265dg7aB//2uUsGAgXDyBBw9avkarVTGCNkyGc2xAQEZI2RdXO4bIXuPhN0nid5zmbRbCQVSg0qrpvzAZpRsFsj5szf49suNBXKch0lN0bH9n7Ns/+cswZV9aN2+Cj2b1qZHk9pcunGb/y1ez+q9x55qTaLw8HBxwvhgV5AVkgAt7FQqy5+B+vlBl66wZjXcekhf2N3Yf7ctD6H/LulWvjzcvfvfNp06QalSMOM7y9ZnZUwaDYZyfhmLFgQGovcPyDJCNuVmPHEnzmeMkI0r+LN0jYsDQSPb4hrkxdaQMyxduL/Aj/koF8/f4uL5W3h6OtOsVSVata3C/A8Hk5CUwp/bDzLx97Ukpsol+4oSdxenjKYZK5fnAD169CgfffQRp0+fJjk5mS5durB+/Xri4uLM29SoUYPmzZsze/ZsS9ZqVSz2GqhUYMlRuFotDH4dIiIyBgQ9zMkTEBcHw9+CA/vB2xuqVoMlf2bcX6oUdO+Z8Xt8nOXqswIme3v0FSpkNMkGBmHwy2GE7LFwYvbmfoSspTiUdid4dAfsirmw+Pc97Nx+4ake/1Hi4pJZt+YYG/4+Tq065WndvipDu7ZiUKfmHLkUxrh5Kzl44ZrSZYqnwMPFCbUNLBCTpwDV6XT0798fR0dHJk+ejJOTE0dtsOlu8+bNHDlyhDFjxihdCqjVGC3ZhNvzWfAqDZMmZh9Ne7/UVPh6Orz0ErRomTEKd+UK+Cck4/5+/SHsGuzcAf4B8MqrUK4cxMVmnNnutZ2VaswjZP0DMpbEu3+E7L1Ukk7cIPZQGHcPX8vXCFlLca1UmqCR7TBp1Xz71QYuno9UrJZHMRpMHDt8jWOHr+Ht40HLNlVo1jKYTVPf407sPeas38E3K0IwynVwbZani7PV939CHgP06tWrREREMGPGDF577TUAevfuzWeffVYgxSklJCSEn3/+uXAEKGByc7XMjgICodMzsHYN3MzF6M6bN2D6V9lvb9YcKlaECZ9mjNZ9e2TGGe2330DNWjDodbgVCVdDsz/WCvw3QjZjwE+WEbJxKdzbd5XYA1eJP1l4pmiUaBpIhcHNSE5OZ/K4VcTEWMfqQJG34lm6aD9rVhymQeNA2nSoyrhXu/PBC8+w9cR5xs5dydVbUUqXKSyspIeFPtMUlqcAjYrKeCN7eHj8twOtFq2NrJSTlJSEi4uL0mVkY7rv9c43tRoGDYbrEbBhff734+4OL7wAf/8FtyOhVeuMATLz5kJsLJw7CzVqZIzM/fmnJ6+7gJkAY8lSGAID0P+76Pp/I2T/XUN263li9l4h6XLh/CAv06s2ZXrW5vatOL74dA3p6da3iEFamp7dOy6we8cFAoK8aNWuCh0bVKPTD9W5GhnN9GUbWbT1gNJlCgvxLVVM6RIsItfJN2zYMBYvXgxA//79AWjWrBnNmzdn6tSpWfpAH7Rw4UKGDx/Ohg0bWLVqFStWrECn0/Hcc8/x5ZdfkpyczIcffsjGjRvN+//ss8/Mc4TCwsKoVasWkyZNQq1WM3v2bKKjo6lbty5fffUVVatWzXK8HTt28L///Y8TJ06g1Wpp1qwZEyZMoFKlSuZtpkyZwtSpU9m/fz9fffUVISEh+Pn5Ub16dfPz9Pz3gxQwP7+ZM2fy119/cenSJVJSUqhUqRKjRo2iZ8+euX0p88zk7IJJrUb1JE1aDg4Z/ZgAP/2c8zZjP8n48/uZcPwhIyRfeiUjKDP7T729M5p3Y2P/2yYiAny8819rATKPkPW/bw3ZzBGyaTpSIu+RsPcUMbsuk3orXulyH0llp6HC680p0cif0ycj+P7rzY9/kBUIvXyH0Mt3WL7oAE1bBtO6XVV+GNmPL4c8z6o9R/l0/mruJiYrXabIJ61GTUkPN6XLsIhcB+jAgQMpU6YM06dP54033qBu3bp4eXmxf3/uR/h98MEHlC5dmjFjxnDo0CHmz5+Ph4cHBw8exNfXl/Hjx7N582ZmzJhBlSpVeOmll7I8fvHixSQmJjJkyBBSU1P58ccf6dGjB3v37sXr32a27du306dPHypUqMBHH31ESkoKc+bMoVOnTuzYsYPy5ctn2eeAAQMICAhg/PjxmEwmatasSWRkJNu2beOnn7KfQf3444907tyZ559/nvT0dFauXEn//v1ZsmQJnTp1yvVrkScqFSZ3d1SP+JLyWHp9xkpCOakYnBGEx45B4j2IechcxFq1oF49mDI54xqSmR5sgdBqC80Iu1yNkD15gehdl57KCFlL0bo5EPROe1z8SxCy8RQr/jyodEkWd+9eKpvWnWTz+lNUr+lL6/ZVebV9E15q04hT164z4fe17DhReAZJidwpU8Kz6K2F27BhQ9LS0pg+fTpNmzY1n3HlJUC9vLxYtmwZKpWK119/natXrzJjxgwGDhzI119/DWQEWs2aNVmwYEG2AL169SpHjhyhTJkyALRv35527drx7bffMnnyZADGjRtHsWLFCAkJoVixjGaCrl270rJlS6ZMmcKPP/6YZZ/Vq1fnl19+yXJbUFAQ27Zto2/fvtmew+HDh3G6b1DP0KFDadWqFbNmzSq4ACWjX079JAGq08Fv83O+b+CgjADdsC7rUn73c3SEV/rB1i1Z+zZv3cq4CkhQUMaCCw6OEFwRTp3Kf61PwDxC1j+j//K/EbJGdElpJF+K+XeE7JWnPkLWUhx9PKg4ugN2Hk78Pm83+3ZdUrqkAmUymTh1IoJTJyIoWcqNlm0q07x1ZdZMHEFMfCLzN+9m6p8bZf1dK+Fb0jaab+EpzwPt169flqWb6tWrx8GDB+nXr5/5No1GQ+3atTlx4kS2x3ft2tUcnpmPr1+/PiEhIUyePJnIyEhOnTrFyJEjzeEJGSHZpk0bQkJCsu1z4MCBeXoO94dnXFwcBoOBJk2asHz58jztJ6+MxYplXdTgaevdB0xGWLUy6+0H9sOzveD/hsOBg1CpEjg5/zdat4AZnZ3/vQbmvwN+yhTOEbKW4lbFh6CRbTGqYPrU9Vy5dEfpkp6q6Kh7rFx6iL9WHaVuA3/adqjKe88/w4hn27PnzGXG/LKC8xG3lC5TPEI5r+JKl2AxTzVAfX19s/zu7u4OQNmyZbPdnlOfakBAQLbbAgMDWb16NQARERFAxhnkg4KDg9myZUu2gUIPNuk+zsaNG/nqq684deoUaWn/Tf4u0DUdDQaMxUsU3P4fJygoY7DQjO8g7YEJ72lpMONbePlVaN06Y+7o3F8KLOyNHh4ZzbEBgTmPkN1/ldj9hWuErKWUbFmR8v2bkJSUxuefriYutuj2A+p0Bg7svcyBvZfxK1+CVu2q0LxJRfbOGMv16LvMWLWFX9Y/pMtCKMq3VHF0egN22iI2jeVJaR4y7yen201PaZknpzzMsdy7dy8vvfQSTZs25auvvsLb2xs7OzsWLlzIsmXLCrBKMBYvwG9t837N+HmYy5dh6OsPv//atYwruViYeYRsQID5KiWmf1sWTGk6UmOSuLftPDF7Cu8IWYtQQdk+9fDpWoObN+4yecIa9DrrP5u2lPCwGP74dTcr/jxI4+YVadO+Kl+98QKT+j/LuoMn+fjXldyOLZglE0XeZTThFpJBEk/IquafhObQP3flyhX8/PwAKFeuHACXMxc/v8+lS5coUaLEE01TWbt2LY6OjqxcuRIHh/+uZ7dw4cJ87zNXNBqMJUoW7DEKAZNKhdHHx7yGrCGoIqb7R8jevkfCvlPE7L5M6s3CPULWUtT2GvyHtsSznh/Hj4bx44x/lC6p0EpOTmfr5jNsCzlDpSplaN2uKs81r0evZnU5fz2SyQv/Zt2Bk0qXWeT5eRW3iUUUwMoCdN26ddy8edPcD3rkyBEOHz7MsGHDAPD29qZGjRosXryYd9991zwN5ezZs2zdupUXXnghV8fJDNm4uLgsU1k0Gg0qlQrDfSNQw8LCWLdunQWe3aNlNlXakowRsuXQBwSar1SSdYRsglWOkLUUrYcTFd9ph7NfcTauO8ma5QV0TVgbYzLB+bM3OX/2JsWKu9D83/V3F44dSvy9ZBZtO8CkhX+RnJqudKlFUmU/H6u/jFkmqwpQf39/nnnmGQYPHkxaWhqzZ8+mePHijBw50rzNpEmT6NOnDx07dqRfv37maSzu7u589NFHuTpO7dq1Afjwww9p164dGo2G3r1707FjR2bNmkXv3r15/vnniYqK4pdffsHf358zZ84UxFM2M3l4YHJwQPVgH6QVMdnbYyhfwXyFEkP58hkjZI1G9ElpJF+OIfaodY+QtRTHsp4Ej+6Axs2ReT/v4OB+61zVSWmxd5P4a9VR1q89Tu265WnToSrDerRhSJeWHLxwlXHzVnHkUpjSZRYZLo72lJVRuMp46aWXUKlU/Pjjj0RFRVG3bl2mTZuGt/d/k/Zbt27NihUrmDJlCpMnT8bOzo6mTZvy2WefUaFChVwdp3v37gwdOpSVK1eydOlSTCYTvXv3plWrVsycOZNvv/2WMWPGUL58eSZMmEB4eHiBByiAwccHrZIjcfPooSNkDQZ0CakknbxJ7OFr3D14zSZGyFqKe/UyBI5ogwGYNnkd10JtuH/3KTEYjBw5dJUjh67iU8aTVm2r0KRFRf6ZNprIu/HM/ns736/eKuvvFrDKfj5Kl2BRqri4uELfm3v/SkQjRoxQuhxlmEw4rliOw77Cu0i70d0DfWBARmhWDM4+QvbSHWIP/jtCVj6nclSqTSX8+jUm8V4Kk8avJiG+6DVdPy0OjnY0bBJI2w7V8CnjSVq6jpCjZxkzdwURd+4+fgciz/q1b8KMt16WJlzxlBkMGH0Kz7e3jBGyJc19l/qgiv+NkE3XkRqTnDFCdm8oSUVsrmK+qFT49q2P9zPVuB4Ww/8mrUUvZ+UFKi1Vx65t59m17TyBFUvTul0VOjeoQZeGNbhyM4ovl6xn2c4jSpdpU6qWL4POYMDeRtZPt41nURRotRgemC/7ND1uhGzq7XvE7z9NzO5LpN4oGiNkLUVtryVgWCs8avty5NBVfp61VemSipwrl25z5dJtli0+QLOWwbRqV5Wf3xvIN8NeYvmuw3z62xrik6Q14ElVr1AWOxsZgQsSoFbF4FMGE/A0Gj8yR8ga/DMG/DxshGzM7svoivCE/idl5+lExVEdcCrryd9rjrFu9UMW8RdPRUJ8Chv+OsGmdSepUascrdtXZUCn5rzargnHQyOY8Nsadp+27aUTC1K1CmVspvkWrKQPVPzH7fOJqO+/8omFZBsh6+cHdnb/jZANjyX2WETGCNlkGf5vCU5+xQl+rz1qZwfmztnO0UPXlC5J5MCrtDst2lSmeatKODraER2fyK8bdzFt2UZpZs+DEu6uXPnjf0qXYVESoFbG+ZefsTt39on3Y3RyxhDgn3FZr6CKGMuWzbhmqMGA7l4aiaHRxB65xt0D12SEbAHwqOVL4PDW6Iwmpk1ex/XwGKVLEo9hZ6+hfsMA2naoRrnyJUjX6dl56iJj567g4vXbSpdX6LWuVZnVE99SugyLkgC1JgYDDiGbcQzJ+3Ufje6Za8j+G5ilSwP/jZBNvBzF3QNXiT8ZISNkC5hX+yqUe7khCfEpTBq3isTEVKVLEnlU3r8krdpWoWHjQNQaNeFRMXy7IoT5m/YoXVqh9dGLXRj9QiebWYUIJECti9GI5tIlXOf8+MjN/hshm7mGbPYRsonnI4nee0VGyD5NahXlXm5I6fZVuHY1ii8n/S3zDq2ci4sDTVpkrL9boqQbySlprN1/gnHzVhIVn6h0eYXKmkkjaFE9GLVa+kCFUtLScP94DKr7Fts3qVQYvX3+7b8MxBAYhMnVNcsI2YSzt4iWEbKKUTtqCfy/1rjXKMuh/aH8+tN2pUsSFqRSQZVqZWndrirVa5XDZDJxNvwmk/74i81HCn6RlcJOo1YT8edXODvYK12KRUmAWiHXb6ZnjJINCEQfGIjePyDjgtdGI4ZUHSk344k/dYOY3ZdJj0lSutwiz664M8GjOuDg48HalUfYuE4WNLdlxUu40qJ1JVq2qYyLqyOx95JYsGUfXyz8m9T0onnR75r+vuz8NndLqVoTCVBrk3nmqVL9N0I2IpbYozJCtjByLl+Ciu+1R+1kz5wftnHimKy7WlRotWpq16tAmw5VCQwqjU5v4MC5UD7+dSUnQiOULu+pGtq1Ff97vTdqtVrpUixKAtTKmAxGdAmpXF9+mNhD1zClSx9aYeVZ14+AN1uSbjDy5Rd/c/O65acfCetQtlxxWrWtTONmFbGz03AzJo7Za7fx/ZqisWjGr6MH0qNpbZsaQAQSoFZJdy+VEyP+VLoM8Qiln6mGb9/6xN1NYtKnq0hOlJYBAY5OdjRuGkTr9lXx9vEkNU3HxsOn+XjuCm7ExCldXoG5MH8ypYu5K12GxUmAWqlTH60kLTJB6TLEA1QaFeVebYxXm0qEXr7NV5P/RgbaipxUrORN63ZVqF2vAirg0s3b/O/P9azabVurUZUrVYxTv0xSuowCIQFqhUwmE2G/7SN6+0WlSxH30TjZETiiDW6Vfdi35xK/z92ldEnCCrh7OGVc9LtdFTw8nElISmHpjkNM+G0NianWe/3fTAM6NePrYS+itqEl/DJJgFohk8FI3LFwrny/XelSxL/sS7pScVR7HEq7s3LpIf7ZdFrpkoSVUatV1KzjR5v2ValUpQx6vYGjl8MYP381+89Z7wXVF3/8Bh3qVUOrsa0BRCABarUMaXqOD1+ESZbZU5xLQEkqjmoP9lpmf7+FMyevK12SsHKlfTxo2aYyzVpWwsFBS1TcPX5ev5Ppyzdb1eIb9lot4Yun4Whvp3QpBUIC1Ipd/GozCadvKl1GkVasfnn832hJmk7P/yb9xe1bslCFsBx7ey0NGmesv1u2XHHS0vVsO3Gej+eu4MqtKKXLeyxbXP/2fhKgVspoMBK97QLhCw4oXUqR5d21Br7P1yMm+h6Txq0mNUVG2oqC4x9YilZtq9KgUQAqtYprt6OZvmwTC7fsV7q0h5o8+DmGdGmFnda2pq9kkgC1YulxyZx8Z6nSZRQ5Ko0av/5NKNWyIhfP3+Lr/61XuiRRhLi6OdK0eUVad6hK8eKuJKWksXrvMcb9upK7iYXr2rzH50ygQumSSpdRYCRArdyZ8WtJCb+rdBlFhsbZnqC32+IaXJpdO86z6Le9SpckiiiVSkW1Ghnr71ar6YvRaOL0tetM+H0t246fV7o8AnxKcfTHT5Uuo0BplS5A5J/JYMSztq8E6FPiUMqNiqM7YF/SlSUL97N9y5Nfl1WI/DKZTJw+eZ3TJ69ToqQrLdpUpmXryqz67C3uJiTy2+a9TFm8nnS9MuvvdqhXFaPRaHPL991PzkCtmMlkIiX8Lmc//UvpUmyea5AXQe+2AzsN338bwvmzMnhLFD5aOw1162esv+sf4IVOp2fP2ct8PHclZ8Ke7nt289RR1AuugEYCVBRmJ95Zii6u4Po+SjUOpFyXWjiXLY7WxZ7U6ETiTl/n2qrDpN7+bzUkF9/iBL/eCveg0qTfS+FmyBnCVh8BY9a3mNpeQ6NvXyX2VATnZxf+tUCLN/anwustSE3TMeWzNUTduad0SUI8Vjm/ErRsW5nGTYPQaDXciI5l5up/mLNuZ4Ef27dkMU7Ptc3Vh+4nAWrlTEYT1/88xO3NBdOcGDSgBeV71iXtbiJRB0PRJ6fjVqEkxWuXx5CazuExy0gKj0HjaEeTWa+h0qiJ3HEeZ9/ilKxbgYvzdhKxNuvSZEGvNcO7VWX2jfgDQyG/eoxPj1qUfa4OUbcT+OLTVaSmFs3LUQnr5eRsT5NmFWnToSqlvNxJSUtnw8FTfPzrSm7dLZhpV2/3as+nr/Ww6bNPkD5Q66eCEi0rFkiA2ns649etNil3Ejjw7sIsYVeuex2CB7XEr0cdzn3/DyXr++NQ3JXDY5YSf/4WAHU+e46yHapnCVBX/1KU616H09M3FOrwVGnVVBjUjBJNAzl3+gbffbVR6ZKEyJeU5HS2hpxha8gZKlXxoXX7qjzbrA49m9bhwvVIJi9ax9/7T1j0mC+1aYgK21u670ESoFZOpVLh7FsMJ99ipFj4clmOXu6oNGriz93MFnbRh68SPKgldu5OADiUdAPg3pU75m0SLt+mXNda/z1IraLK8PZEH75K1P4rFq3VkjQuDgS90w7XwFJsDTnD0oWFd56dEHlx4dwtLpy7hWcx54z1d9tWYcGYIcQnJrN420EmLlhLcuqTfbGtXM6bKuXLWKjiws22z6+LCJPBSIlmgRbfb/KtOIw6PR5VyqBxss9yX8n6/gDEnsy4MHBadEa/oGtAKfM2bgFepEb911/o16MuTt4eXJiz3eK1WopDaXeqTuiGi39JFv6+R8JT2KS42GT+Xn2MD99dzJxZW7hzI543u7cmfOE0Nkx5l/rBFfK9794t66M3GCxXbCEmfaA2QncvlRMjl2QbsPOkynWvQ8UBLUiPS8rSB1qsRjlu/nOai7/swGQ0oXGyp8n3r4EKbu+6gHOZYpSs72/uA3Xy9qDRt69w6ded3NhcOBdad61UmqCR7UCr5rvpm7h0IVLpkoR4anzKeNKybWWatgjG3l7L7dgEfvx7OzNWbcnT+runfplIuVLFC7DSwkMC1IZcnB5CwqkbFt+vV9OKVBneDq2zg/m2uLM3uPzHHnN/J4CLXwmCB7fCPcgLXUIqN/45Q9iqw2A0UWdCL1RaDUc/WY57xdJUGtoGV/9SpMUkEvrnfiK3nbN43XlRomkgFQY3Izk5nS8mrOZuTJKi9QihFAcHLQ2bBNK2QzV8yhYjLV3HlmPnGDN3BWG3Yx752HrB5dky7f2nVKnyJEBthNFgJO5wGKGzd1h0v/4vNKRCn4YZIbfjPPqkNFz9SxE8sCWu/iU59eU6og9dfeQ+fNpVpdLQNhx8dyFpd5No+tNAEq9GcW3lYUrW98evex0OffAnCZduW7T2XFFBmV51KNOjFrdvxTHp01Xo063nahdCFKTAIC9atatKvYb+qFQQeiuKL5dsZOmOQzlu/82wF+nXoQlajW2uffsgCVAbYtQbOPH2EouNbi1Wsxx1P3uO8LVHuTQv68Wh7T2dafrjANJiktg3/LeH7sPew5nGM/sRvvYo15YfomynGlQa2oY9Q38lLSYRgCazXiPhyh3OfP10R7qq7DT4D2lO8Yb+nDoRwaxvNj/V4wthLdzcHGnaMpg27aviWcyFxORUVu4+wvjf1hD37/q7bk6OXPxtMk4O9o/Zm+2QUbg2RKVRU7yRP1HbLlhkfyXqVgAg9lT261umxyWTdD0W90AvNI52GFJ1Oe4jeEgrUmMSCVt1BADnMsXQ3UsxhyfAvatRuJQtZpGac0vr5kDQO+1x8S9ByIaTrFiS8zdqIQTcu5fKpnUn2bz+FNVr+dKmXVVe69iMl9s14WRoBBN+W0NgWS+bve7nw0iA2hITlO5Y1WIBqtZmDNK283DK8X57DydMBiPGh1zUu2R9f7waB3F4zFJMhv+2UT9waSO1nQbTU2wHcfTxoOLoDth5OPH73F3s23P56R1cCCtmMpk4dTyCU8cjKOXlRos2lWneqjJrP38bnV6PyWRCpbL9+Z+ZZBqLDVGpVTj6eOBezcci+8scIOTXow4a56zNMmU71cCxpBvxFyMx6bMPWdc42VPpjTZc33AiS99m0vW7aF0c8KicUaPG0Q7PKmVJvv50FsR3q+JDlU+7oXZ14Kv/rZfwFCKfou7cY+WSQ3w4chGb1p3ETqu16YXjcyJnoDbGZDBSulM1Es7cevzGj3F77yXKPlODYtV8aTqrP1GHQtEnpeEW4EXxmuUwpOm4NC/ndTWD+jXDZDRxZeG+rPvcdYHAV5pQ88NuRO66QLHqvmhdHAj/+/gT1/s4JVtWpHz/JiQlpfH5p6uJiy1c104UwhrpdAZKlHLFYDCi0RStAJVBRDbq1EcrSYtMePyGj6HSavDrUQevphVxKVsMlVZNelwysaevc23FIZJzWP3Io7IP9b54nhNfrCHmaFi2+92CSlNpSGvc/EuSFpPElcX7uL3TMs3OOT8JKNunHj5da3Dz+l0mT1iD/iHNzkKIvPH0dGby9L6oi1h4ggSoTTIZjERtv0D4HweULkVxansN/kNb4lnPjxNHw/hx5halSxLCpnR/ri6du9Uqcs23IH2gNkmlUVOyRcVs/ZZFjdbDiUpju+BZtxwb/zoh4SmEhdnZa2jdtmqRDE+QALVZKq2Gkq0qKl2GYpx8Pak6oRuOvp78OmcHa1YeUbokIWxOi1aVcXYpul/UJUBtlQpKd6qGSlN0hpRncq9RlsrjuoKTPVM//4tD+0OVLkkIm6O109C5e63Hb2jDJEBtlEqlwt7TmeJNLH+VlsKsVJtKVHy3PUkpOj7+cBnh1x69dqcQIn+atQjG1c2xSM37fJAEqA0zGU2UebY2qIvAG1ylwvfFBpTv34TrEXf5aNQS7sWnKF2VEDZJq1XTpUdtpctQnARoDqZMmYKnpycxMU/37KVr1640adLEYvtTqVU4lHSlRJMAi+2zMFI7aAl6uy2lO1XlyMFQvvh0dZ4uvySEyJvGzSri7uFUpM8+QRZSsHmZZ6Ex+0Itfq3QwsDO05mK77XHqYwnf68+xro1x5QuSQibptao6NqzDiYTFPH8lAC1dSq1CodSbpRoGkjMbttats7JrzjB73VA7WzPnNnbOHb4mtIlCWHzGjcNolhxF6XLKBSkCbcIMBlNlO1dB5XWdv65PWr5UuWTLhjtNUyZtFbCU4inQK3+9+zTBluz8sN2PlELQExMDAMGDKBcuXL4+/vz4Ycfkpqaar5fr9fz5ZdfUrt2bby8vKhRowYTJ04kLS0t275++eUXGjdujJeXF5UrV2b06NHExcU9toatW7fi4+PD4MGD0ev1+XoeKrUKO09nSrYKztfjCxuv9lUIGtmOhKQ0Pn5/GdfDn85C9EIUdY2bV6RESTdURWFgYi5IgD7CgAEDSE1NZfz48XTo0IGffvqJkSNHmu8fMWIEkydPplatWkyePJlmzZrx9ddfM2jQoCz7mTJlCqNHj8bb25vPP/+c7t27M2/ePJ577jl0upyvowmwceNGXnrpJXr27MmcOXPQap+sxb3Ms7VR21txq71aRblXG+H3aiPCrkUz9r0lJCamPv5xQogn5uBox3PPN8D0NK89WMhZ8adpwStfvjyLFy8GYMiQIbi7u/PLL78wYsQITCYTixcv5rXXXmPGjBkAvP7665QqVYqZM2eyc+dOWrZsSXR0NN988w1t27Zl+fLl5iWvgoODef/991myZAmvvvpqtmOvXbuWwYMH8/LLL/PNN9888VJZKpUKrYsD3l2qc3P18SfalxLUjnYEDm+Ne/UyHNh7iXlzcr4KjBCiYHTuVgtnF/siP/L2fnIG+ghDhgzJ8vvQoUMBCAkJISQkBIDhw4dn2eatt94CYPPmzQBs376d9PR0hg0bliUE+/fvj7u7u3m7+y1fvpxBgwYxYMAAvv32W4utM6lSq/DuVgP7kq4W2d/TYl/chSrjuuJW1YfVyw5LeArxlJUs5UaHZ2oU2TVvH0ZejUcICMg6f9Lf3x+1Wk14eDgRERGo1eps25QuXRoPDw8iIiIAzH9WrJh1XVp7e3vKly9vvj9TWFgYQ4cOpUePHkybNs3i3/ZUKhXlXmpg0X0WJOcKJagyoTv2Xm789P0WNq0/qXRJQhQ5vV9sCHLimY0EaB7kFGaWDrjSpUvTqFEjQkJCOHbM8nMaVRo1xeqVx62qj8X3bWmedf2o/HEXDHZqPp+wmhPHwpUuSYgiJ7iyN3XqVShyF8vODXlFHiE0NDTb70ajET8/P8qVK4fRaOTKlStZtrlz5w7x8fGUK1cOwPznpUuXsmyXnp5OWFiY+f5Mjo6O/PnnnwQEBNC7d2/OnTtn6aeFyWCkfL/GhXqh+dLPVCNwRBviElIY+94Sbt2IU7okIYoclUrFi682wWiQlb1yIgH6CD///HOW3+fMmQNA+/bt6dChAwCzZ8/Oss2sWbMA6NixIwCtW7fG3t6en376KcvotT/++IOEhATzdvfz8PBg5cqVlCpVil69enH16lXLPSkyzkIdvN0p1bayRfdrCSqNivIDmlDuxQZcuXybT95fQnJyutJlCVEkNWsZTBnf4qjl7DNHMgr3EcLCwnjxxRdp3749Bw8eZOnSpTz//PPUqFEDgJdeeon58+cTHx9Ps2bNOHLkCIsXL6Zr1660bNkSgJIlS/Luu+8ydepUevfuTefOnbl06RJz586lbt269O3bN8djlyhRglWrVvHMM8/Qs2dPNm7cSJkyZSz6/Mr2rsvd/VfR3yscU0E0TnYEjmiDW2Uf9uy8wB+/7la6JCGKLEcnO3o9Xx+TySQjbx9CvlY8wrx583BwcOCzzz5j8+bNDBkyhJkzZ5rvnzlzJmPGjOHo0aOMGTOGXbt2MWrUKH799dcs+xkzZgzTpk3j+vXrjB07ltWrVzNgwABWrlyJnZ3dQ49fpkwZ1qxZQ3p6Or169bLo4vYqlQq1nYayz9e12D6fhH1JVyqP74ZrJW+W/3lAwlMIhfXu2xAnZ5m28iiquLg4mRVbxF2YupF75yIVO75LYCkqvtsO7O34YWYIZ0/dUKwWIQRUquLDux92UbqMQk/OQIs4k9GI/5AWqB2Vac0v1qAClT56Bp1axcTxqyQ8hVCYg6MdA4a0kksC5oIEaBGnUqux83TC94X6T/3Y3t1qEDi8NbFxyYx5byl3IuOfeg1CiKyee74+Hp5OsmhCLsgrJFCp1Xi1rYxblaczN1SlUVNhcDN8+9Tj4vlbfPz+UlJTZKStEEoLruxNq3ZVJTxzSV4lAWQ25TYv8KZcjYs9wR90pESzIHZsPcfX/1tfoMcTQuSOvb2W/q+3kjmfeSABKoD7mnL7Ftwyfw6l3KjyaTdcAr34c+E+Fv++t8COJYTIm2f71KdYcWeZ85kHMg9UmKnUarzaVCL20DXunb1l0X27VvQi6J12YKdhxtcbOW/h/Qsh8i8ouDRtO1ZTugyrI181RBaZo3I1LvYW22fxxv4Ef/gM6SYTEz5eIeEpRCHi7GzPoDdaY5Cm2zyTABVZqNRq7Nwd8R/SwiL78+lZi4A3WxETc4+PRi0hOirRIvsVQlhG/yEt8fB0lsXi80FeMZGNSqPGs3Y5Sj+T/yYdlVaN/9AWlO1Vh3OnbzDuw+Wkp+stWKUQ4km161iNWnXKS3jmk/SBiofyfaE+SZejSLx8J0+P07o6EDSyHS6Bpdi6+QxLF+0voAqFEPnlH+hF774NlS7DqsnXDvFwJhOBI9qgdXPI9UMcvN2pMqEbzv4lWDB/j4SnEIWQi6sDb45oh6zj+mQkQMVDqTRqtK4O+L/RMldXo3er7E2V8d1QuznxzZcb2bPzQsEXKYTIE5UKBr3RGlc3R2m6fULy6olHUmnUuFcrg3fXGo/crkSzQILf70iawcj4scu5dFG5xemFEA/XsUtNqtXwlfC0AHkFxWOpVCrKPlcXtyreOdwJZZ6rg/+QFty+k8CH7y0mNibp6RcphHisoGBvevZ++ute2yoJUJFLJgJHtMXBy818i8pOQ8CwVvh0r8mp4+FMGLMCfbrMJROiMCpZyo1hb7cH6fm0GLkeqMg1k8FIWnQi5z77G5VGTcV32+FcoQQhG0+zcukhpcsTQjyEs7M9H33agxIl3aTp1oIkQEWemAxGEq9E4VDSFa27I7/P283+PZeVLksI8RAajZp3PniGgKDSEp4WJvNARZ6oNGpcK3ph1BuZNmU9V6/kbY6oEOLp6jeoOYEVvVGrczGUXuSJfB0ReaZSqdDYaQgKLq10KUKIR+jSozaNm1WU8CwgEqAi33r3bUi9hv5KlyGEyEHDJoH0eK6e0mXYNAlQkW8mk4lBQ1vLmagQhUxQsDf9B7fEZJIhLgVJAlTkm0qlQqWGEaM64VehpNLlCCGAMmWLMfzdDqjUGf9HRcGRABVPRK1WY2en4d0PO1O2XHGlyxGiSPP28WDUmC7Y22tRq+XjvaDJKyyemFqjxt5ey6iPuuBTxlPpcoQokrxKu/PemK44OdnLdJWnRF5lYREajRpHRzveG9MVr9LuSpcjRJFSspQb743tirOLg4TnUySvtLAYjUaNk7M9743tSslSbo9/gBDiiZUo6croj7vh6ipXV3na5NUWFqXRqHF1dWT02K4UL+GqdDlC2LRixV0YPbYrbnJpMkXIKy4sTqNR4+buxHtjuuJZzFnpcoSwSZ7FnBk9thvuHs4SngqRV10UCI1GjWcxZz74pDulvKQ5VwhLKlbchdH/fkGV8FSOLCYvCpTBYCQ1JZ3vpm0kPCxG6XKEsHplfIvxzvudcXGVAUNKkwAVBc5gMGLQG/nhuxDOn72pdDlCWK2KlbwZ/m5H7Ow0Ep6FgASoeCqMRiMmE8ybs4PDB0KVLkfkwM5OQ99XmxMQWBpvH09cXB1JTkrjdmQc2/45ze4d5zAYMi6YrtGoqdcgkHoNAwis6E2JEm6YgBsRMezYeoYtIacwGU057r9x02DsHbScO3Od337Zxt2YxGy19H2lGa3bVWf0iPkkJaU9jadf6NWtX4HBb7ZBpUYWSSgkJEDFU2MymVCpVCxZsI9t/5xVuhzxADc3R77/eShXLkdy62YsCfHJuLg6UrtuBUp5eXDi2DWmTlqJyZSxXNz07weSkpLO6ZPh3LoRi7OLPXXrB1K8hCtHDl3hq8lrsux/wJC2dHimFvv3XODevVRata3GrRt3+fiDRVnC1q98Sb746hVmfbOB/XsvPu2XoVBq3a4KfV9tgsmEXFmlEJHrgYqnJnNdzr6vNsHdw4k1K44oXJG4X2JiKoNe/R6D3pjldrVaxdgJfahVpwK16/pz7MhVUlJ0/PrTFnZuO0Namt687QKHHYyb9AL1GgTSqGlFDuy9BIBKBW3aVWf7ltP8/EMIAJcv3mL4O50JDPLm8sVbGdupVQwZ3pETR69JeP6rZ+96dO5eG8h4HUXhIe0AQhGdu9fmtcEtpB+nEDGZyBaeAEajiUMHLgNQ2scTgNi7iYRsPJElPAHS0vSsX5vxxahKtXLm293cnbB30HL1ym3zbaH//r1kyf9GaXfuVocyZYvx65wtlnlSVkytUfHa4Bbm8BSFj5yBCsU0blYR7zKe/DjjHxLiU5QuRzyESgW16lQAICIs+rHb6//tJzUa/gvjewkppKfpKe/vZb7NPyDj79HR9wAoWcqd519syqI/duXYL1qUuLg4MPSttlSs5KN0KeIRJECFYtRqFeUrlGTcpF7MnvEPoZfvKF2SADRaNc/2boRKBa5uTlSvWY6yviXYvuU0Z05FPPbxrdtVB+Dk8TDzbSYTbN96mvadauHkZE/ivRRatq3G1Su3uXI5EoDXh7Un7FoUIRtOFMwTsxJlfYsxfFRHPDycpb+zkJMAFYrSaNS4uDjw3piu/LlgH7u2nVe6pCJPq9XQ58Um5t+NRhN/rT7Mn3/seuxj23aoQZ16/pw+Gc7xo1ez3Ldg/k4MeiONmgZjb6/l1IkwfvtlGyajiRatq1C1ui9jRi3AwdGOgUPa0qhJRQAO7LvEvDlbsjUX26K6DSowcEgr1Bq1dG9YARmFKwqFzBG6e3ZeYPEf+9DrDEqXVOSpVFCsuCt16wfQ99XmXI+I4ctJq0hJSc9x+zr1/Rn1QQ9iYxMZ/9GfxMUm5eo4bu5OTJ85gE3rj7NiyT4Gv9mexs2CmTdnK5hMDHyjHft2nefXOVst+fQKFbVaRc/e9enUtSZGo0nOPK2EfMURhULmCN0mzYN5f2w3WUO3EDCZ4G5MIv9sOskvs0OoXKUsz/ZplOO2tev688773YmPT+bz8ctzHZ4A/Qe3IT4uidUrDuDoaEfrdtX4e/Vh9u46z97dF1i35jBt2tfAwdHOUk+tUHH3cOLdj7rQsUsNQKapWBMJUFGoqNUqfP2K88mkXlSs5K10OeJfmf2ZVav7ZruvTj1/3v2wO/cSUpg0bhl3bsfner+16/rTpFkwc34IwaA34uXtgVarIexqlHmba6FRaO00lPb2ePInUsgEBXszblIvAgK9zF8ihfWQABWFjkajxtnZnlEfdeHZPvWlL6gQKFY849J0ekPWaS516vnzzgfdSUpM5fPxy7gdGZfrfTo42jHojXaEbDrJpQu3styntdNk/7sNdTap1Sq69KjNqI9kTVtrJv9qolBSq9WoVCo6dqnJ2Ak98SnjqXRJNq+sb3Hs7bOPK7S319JvYCsAjh/5b2BQrboVzOE5adwyIm/F5el4L77SHLValWVw0u3IePQ6A3Xq+Ztvq1PPH51On6dwLsxKe3vw4bgedO9VF7VaLcvyWTEZhSsKNbVahXcZTz6e+Cwrlx5iW8gZTDZ0JlKYNG5WiS496nLh3E2i78STnJJO8eKu1K7rj5u7E+fOXGf9X0eBjKX8Rn3YA3t7LYfPXKdpi8rZ9hd1J56d23JesjEo2IeOnWsx/X9rSU3VmW9PS9WxbctpOjxTyxzmzVtVYdO6Y1Y/ClelglZtq9D7xUao1SppsrUBEqCi0Mts3nrh5cbUquPHvDk7iItNVrgq23PscCjFirsQXLkMFSv54OhoR0pyOuFhUezddYHtW05j/HfNWg9PF3PA5RSeAGdPR+QYoBqNmiH/14ED+y5x9HD2CwssnL8DrVZjnsayNeQUC39//BSawsyzmDMDhrSictUy5hHnwvrJNBZhVQwGI+npehbO3yNXdRFWoWGTQF7u30wuQWaDJECF1cmcJ3f4QChLF+2XZQBFoeTi6sDL/ZtRr4G/zO20URKgwmoZDEb0OgOrlx9mx9Zz5uZFIZSkUqlo3iqYXi80xMFBK2edNkwCVFg1078jim5ej2XB/D1cvSLr6QrlVPAvycsDmuFXvqT0dRYBEqDCJhgMRjQaNXt2XmDl0kMkJaYpXZIoQlxcHXi2T32at6qE0WiSs84iQgJU2BSDwUhamp6VSw6yZ+cFmfIiCpRKpaJ560r0er6BNNcWQRKgwuZkNp2FXY1m6cJ9XJHLpIkCUCGgFK/0b0a58iWkubaIkgAVNiuzWffMyQhWLz9CRHiM0iUJG1CmbDG6P1eXOvUqmN9jomiSABU2L/ND7sjBUNauPMrtyNwvdi5EJq/S7nR7ti4NGgdIP6cAJEBFEWIwGFGpVOzfc4m/Vx/jbkyi0iUJK1CipCtdetSmSfOKmExIcAozCVBR5Bj+vaLIzm3n2PDXCVmIQeTIw9OZzt1r0aJ1xlKFEpziQRKgosgyGIwYjSb27rrI1s1npGlXABlnnG07VqNVmyqo1CoJTvFQEqCiyMvsIz19IoKQjae4cO7W4x8kbE5QsDftO1WnZh0/TCbp4xSPJwEqxL8yg/TmjVg2rz/J4QOh6PXGxz9QWC2tVk29hgF06FwD33LFZVStyBMJUCEeYDQaUavV3LuXwtbNZ9m57ZysbGRj3NwcadGmMm07VMPVzdH8by5EXkiACvEIRqMJo9HIkUNX2bvzIhfP35LVjayUSgXBlX1o0rwiDRoFolIjoSmeiASoELmQ2bQXezeJPTsvsG/3JWKiZRqMNfAp40mjZkE0aVYRD09naaYVFiMBKkQemEwmTEYTao2aK5dvs3/PZY4evEpSkjTxFibuHk40aBRAkxbB0rcpCowEqBD5ZDRmLMxgNJo4c/I6hw+GcubkdQlThTg62VGjVjmaNKtI5WplABUqFbJGrSgwEqBCWEDmGY7RaOJq6B1OHA3n1PFwbt2MU7o0m+bt40GNWuWoWac8AUFeaDRqOdsUT40EqBAWZjKaMGFCrVZzNyaR40fDOHU8gksXbsm0mCektdMQXNmbGrXKUatOeYqXcMVozPgIU6vlTFM8XRKgQhQwg96IRqsmPV3PuTM3uHg+kiuXbhMRFmNeVlDkTK1WUbZccQKDvKhaw5cqVctgZ6+Vs0xRKEiACvEUGQ1GUKlQq1XodQbCrkVz6UJGoIZevlPk+09dXBwICPIiIMiLoGBvKviXxM5em3GWacoYvCVEYSEBKoSCTCYTRoMJjTYjGO7cTuDi+VuEXY3i5o04Im/G2Wyo2ttr8SnrSTm/EgQEeVGxkjelvNyBjD5ltVolA4BEoSYBKkQho9cb0GjU5vBISkzl5o1YblyP5dbNOG7diCPyVpzVXEXG1c0Rr9LulPb2oLSPB2XLFsPXrwTFirsA/36JkOtrCiskASqEFTCZTP+elanNg2VSUtK5G51I7N0k4uKSic/8iU8x/z0hPqXA+lm1dhrc3Z1w93DCwyPjT3cPZ/PvxUu64uXljqOTvfkxer0hy3MQwppJgAph5TKbgU2Yspy5ZkpOSiMtTU96uh6dzkD6v39PS9OhSzeg02XcrtMZ0KjVaO00aO002Nlp0GrV2NtpsbPXYGevxc5Og729FldXhyzBmFlHZlhr1GpUEpLCxkmAClFEmUymf38y/p4pM4BVKlmIQIhH0SpdgBBCGRkBKeEoRH5Jr70QQgiRDxKgQgghRD5IgAohhBD5IAEqhFDU0aNH6dixI2XKlMHT05OXX34ZT0/PLNvUqFGDYcOGWeyYYWFheHp6MnPmzMduO2XKlGz1KG3YsGHUqFHD/Hteno+wHBlEJIRQjE6no3///jg6OjJ58mScnJw4evSo0mU9sVu3bjF//ny6du1KzZo1lS5HFBA5AxVCKObq1atEREQwYsQIBgwYQN++ffniiy+IjIxUujSz999/P8/1REZGMnXqVE6dOlUgNc2YMYPDhw8XyL5F7kmACiEUExUVBYCHh4f5Nq1Wi6Ojo1IlZVOY6klKSgLAzs4OBweHp3rs5OTkp3o8ayABKoRQxLBhw+jatSsA/fv3x9PTk65du+aqz3HhwoV4enqyb98+PvjgAwIDA/Hz8+Odd94hPT2duLg43njjDcqXL0/58uUZP358lsUi7jd//nxq166Nl5cXbdq0ydaEnFM927Zt45lnnsHPz4+yZctSv359Jk6cCMCuXbto06YNAMOHD8fT0xNPT08WLlxofvzq1atp1aoV3t7eBAQEMHToUG7evJnt9SlbtixXr17l+eefx9fXlyFDhpjvu78PNCcmk4mRI0dSqlQp1q5da759yZIl5mNXqFCBQYMGcf369SyP7dq1K02aNOH48eN07twZHx8f8/MT/5E+UCGEIgYOHEiZMmWYPn06b7zxBnXr1sXLy4v9+/fneh8ffPABpUuXZsyYMRw6dIj58+fj4eHBwYMH8fX1Zfz48WzevJkZM2ZQpUoVXnrppSyPX7ZsGYmJiQwcOBCVSsV3331Hv379OH78OHZ2djke89y5c/Tt25dq1aoxduxYHBwcCA0NNdddqVIlxo4dy+TJkxkwYABNmjQBoFGjRkBG+A8fPpy6desyfvx4oqKi+PHHH9m/fz87d+7MEtZ6vZ7nnnuOxo0bM2nSJJycnHL1uhgMBoYPH86qVatYsGABnTp1AuCrr77iiy++oFevXrz22mtER0czZ84cunTpku3Yd+/epU+fPjz33HP07duXUqVK5erYRYkEqBBCEQ0bNiQtLY3p06fTtGlTevbsCZCnAPXy8mLZsmWoVCpef/11rl69yowZMxg4cCBff/01AAMGDKBmzZosWLAgW4Bev36do0ePmoMjKCiIl19+mS1btvDMM8/keMxt27aRnp7O8uXLKVGiRI41dejQgcmTJ9OgQQP69u1rvk+n0zFhwgSqVq3K+vXrzU3DjRs3pm/fvvzwww+MHTvWvH1aWhrPPvssn376aa5fE71ez9ChQ9mwYQOLFy+mbdu2AISHhzNlyhQ++eQT3nvvPfP23bt3p2XLlsydOzfL7bdv3+abb75h4MCBuT52USNNuEIIq9WvX78syxHWq1cPk8lEv379zLdpNBpq165NWFhYtsc/99xzWc66mjZtCsC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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oq7pEl5HqZFc"
      },
      "source": [
        "###  How many reviews are there for each movie?"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "movie_value_counts = df[\"Movie\"].value_counts()\n",
        "movie_percentages = df[\"Movie\"].value_counts(normalize=True)\n",
        "movie_df = pd.DataFrame({'Count': movie_value_counts, 'Percentage': movie_percentages*100})\n",
        "movie_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "Ii1ApqKmPB7i",
        "outputId": "fab27fe7-159c-4d56-8dc4-c740a33c2c18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Count  Percentage\n",
              "Caligari       38       47.50\n",
              "Metropolis     25       31.25\n",
              "Nosferatu      17       21.25"
            ],
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-90f23e94-3e1d-4be2-bf15-ef7e22c972fd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create reviews distribution by movie pie chart\n",
        "# Data\n",
        "categories = movie_df.index\n",
        "item_counts = movie_df['Count']\n",
        "\n",
        "# Chart\n",
        "wedges, texts, autotexts = plt.pie(item_counts, labels=categories, autopct='%1.0f%%', startangle=180, counterclock=False, pctdistance=0.8)\n",
        "\n",
        "# Increase fontsize of percentage labels\n",
        "for autotext in autotexts:\n",
        "    autotext.set_color('white')\n",
        "    autotext.set_fontsize(14)\n",
        "\n",
        "# Add lines between slices\n",
        "for wedge in wedges:\n",
        "    wedge.set_edgecolor('#F0F0F0')\n",
        "    wedge.set_linewidth(1)\n",
        "\n",
        "plt.title(\"Number of Reviews per Film (n = 80)\")\n",
        "plt.savefig(\"NReviewsMovie_Perct.jpg\", dpi=250, bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "REAtUhFS7wr0",
        "outputId": "3b0c4c1b-12e6-437b-b0d5-48a5c837bb7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZptAHobTqN_E"
      },
      "source": [
        "###  What are the languages?"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lang = df['Language']\n",
        "lang_value_counts = lang.value_counts() # Count occurances of each language\n",
        "lang_percentages = lang.value_counts(normalize=True) # Count occurances of each language compared to the whole\n",
        "lang_df = pd.DataFrame({'Count': lang_value_counts, 'Percentage': lang_percentages*100}) # Create dataframe with columns for each value and multiply by 100 to get percentages\n",
        "lang_df = lang_df.reset_index()  # reattach a numbered index column to the left\n",
        "lang_df = lang_df.rename(columns = {\"index\": \"Language\"})  # change the old index to a column named \"Language\"\n",
        "lang_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "usTFDNIKbHis",
        "outputId": "c57db8c8-a5d8-49a4-bc10-04d4dce9790a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Language  Count  Percentage\n",
              "0  English     34        42.5\n",
              "1   German     32        40.0\n",
              "2   French     10        12.5\n",
              "3  Spanish      4         5.0"
            ],
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              "        document.querySelector('#df-2ea455f2-da59-482f-81b7-6081b3397a43 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2ea455f2-da59-482f-81b7-6081b3397a43');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0bd5808f-6fe6-43c1-a1f9-8b62ca71d0d6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0bd5808f-6fe6-43c1-a1f9-8b62ca71d0d6')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0bd5808f-6fe6-43c1-a1f9-8b62ca71d0d6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create reviews distribution of reviews by language pie chart\n",
        "# Data\n",
        "categories = lang_df[\"Language\"]\n",
        "item_counts = lang_df['Count']\n",
        "\n",
        "# Chart\n",
        "wedges, texts, autotexts = plt.pie(item_counts, labels=categories, autopct='%1.0f%%', startangle=180, counterclock=False, pctdistance=0.8)\n",
        "\n",
        "# Increase fontsize of percentage labels\n",
        "for autotext in autotexts:\n",
        "    autotext.set_color('white')\n",
        "    autotext.set_fontsize(14)\n",
        "\n",
        "# Add lines between slices\n",
        "for wedge in wedges:\n",
        "    wedge.set_edgecolor('#F0F0F0')\n",
        "    wedge.set_linewidth(1)\n",
        "\n",
        "plt.title(\"Number of Reviews by Language (n = 80)\")\n",
        "plt.savefig(\"NReviewsLang_Perct.jpg\", dpi=250, bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "Up-AX-rFJep1",
        "outputId": "ff3fc16d-19ab-461a-a1e9-48fda79aaa3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's visualize this data with a bar chart\n",
        "plt.figure(figsize=(6, 3))\n",
        "by_language = sns.barplot(data=lang_df, x=\"Count\", y=\"Language\", color=\"#003f5c\")\n",
        "by_language.set(ylabel=None)\n",
        "by_language.bar_label(by_language.containers[0], fmt=' %.0f')\n",
        "by_language.set_xlabel(\"Number of Reviews\")\n",
        "plt.title(\"Reviews by Language\")\n",
        "plt.savefig(\"N_Language_Bar.jpg\", dpi=250, bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "KFOY1cUg86IC",
        "outputId": "bd00ec52-feb6-4357-d390-18ad858eb846"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###  What are the countries?"
      ],
      "metadata": {
        "id": "lY6sQ0ZheUSc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "country = df['Country']\n",
        "country_value_counts = country.value_counts() # Count occurances of each country\n",
        "country_percentages = country.value_counts(normalize=True) # Count occurances of each country compared to the whole\n",
        "country_df = pd.DataFrame({'Count': country_value_counts, 'Percentage': country_percentages*100}) # Create dataframe with columns for each value and multiply by 100 to get percentages\n",
        "country_df = country_df.reset_index()  # reattach a numbered index column to the left\n",
        "country_df = country_df.rename(columns = {\"index\": \"Country\"})  # change the old index to a column named \"Language\"\n",
        "country_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "hAmbwnZNefAU",
        "outputId": "e8cf736a-6648-44d6-9e8d-4c2145d9e97a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Country  Count  Percentage\n",
              "0     DEU     33       41.25\n",
              "1     USA     29       36.25\n",
              "2     FRA     10       12.50\n",
              "3     GBR      6        7.50\n",
              "4     ESP      1        1.25\n",
              "5     CHE      1        1.25"
            ],
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              "      <th>Country</th>\n",
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              "      <th>0</th>\n",
              "      <td>DEU</td>\n",
              "      <td>33</td>\n",
              "      <td>41.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>USA</td>\n",
              "      <td>29</td>\n",
              "      <td>36.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FRA</td>\n",
              "      <td>10</td>\n",
              "      <td>12.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GBR</td>\n",
              "      <td>6</td>\n",
              "      <td>7.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ESP</td>\n",
              "      <td>1</td>\n",
              "      <td>1.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>CHE</td>\n",
              "      <td>1</td>\n",
              "      <td>1.25</td>\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ead21e15-517f-4d02-9c9d-7755c903e52f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ead21e15-517f-4d02-9c9d-7755c903e52f')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ead21e15-517f-4d02-9c9d-7755c903e52f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since there are more countries here and some have very few reviews, a bar chart is clearer, so let's do that here:"
      ],
      "metadata": {
        "id": "nFqDNtchLA4O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's visualize this data with a bar chart\n",
        "plt.figure(figsize=(6, 3))\n",
        "by_country =sns.barplot(data=country_df, x=\"Count\", y=\"Country\", color=\"#003f5c\")\n",
        "by_country.set(ylabel=None)\n",
        "by_country.bar_label(by_country.containers[0], fmt=' %.0f')\n",
        "by_country.set_xlabel(\"Number of Reviews\")\n",
        "plt.title(\"Reviews by Country of Publication\")\n",
        "plt.savefig(\"N_Country_Bar.jpg\", dpi=250, bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "ANSGW63ZK_3l",
        "outputId": "618cdc5c-05a1-4caf-a793-480bb8283c9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###  What are the types of publications?"
      ],
      "metadata": {
        "id": "KGkuGsXHfgKX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pub_types = df['Publication Type']\n",
        "pub_types_value_counts = pub_types.value_counts() # Count occurances of each pub_types\n",
        "pub_types_percentages = pub_types.value_counts(normalize=True) # Count occurances of each pub_types compared to the whole\n",
        "pub_types_df = pd.DataFrame({'Count': pub_types_value_counts, 'Percentage': pub_types_percentages*100}) # Create dataframe with columns for each value and multiply by 100 to get percentages\n",
        "pub_types_df = pub_types_df.reset_index()  # reattach a numbered index column to the left\n",
        "pub_types_df = pub_types_df.rename(columns = {\"index\": \"Publication Type\"})  # change the old index to a column named \"Language\"\n",
        "pub_types_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "5c25ecdb-85ca-47ee-d0de-f786dd04a5a5",
        "id": "CiQT0VFSfgKX"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Publication Type  Count  Percentage\n",
              "0              Trade Journal     39       48.75\n",
              "1  Newspaper/ News  magazine     20       25.00\n",
              "2                  Criticism     19       23.75\n",
              "3               Fan Magazine      2        2.50"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9fb6136f-4799-4685-8497-d302c209fe5c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Publication Type</th>\n",
              "      <th>Count</th>\n",
              "      <th>Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Trade Journal</td>\n",
              "      <td>39</td>\n",
              "      <td>48.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Newspaper/ News  magazine</td>\n",
              "      <td>20</td>\n",
              "      <td>25.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Criticism</td>\n",
              "      <td>19</td>\n",
              "      <td>23.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fan Magazine</td>\n",
              "      <td>2</td>\n",
              "      <td>2.50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9fb6136f-4799-4685-8497-d302c209fe5c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9fb6136f-4799-4685-8497-d302c209fe5c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9fb6136f-4799-4685-8497-d302c209fe5c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e37481d4-0f22-48c7-8a36-faead372d2d3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e37481d4-0f22-48c7-8a36-faead372d2d3')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e37481d4-0f22-48c7-8a36-faead372d2d3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's visualize this data with a bar chart\n",
        "plt.figure(figsize=(6, 3))\n",
        "by_pubtype = sns.barplot(data=pub_types_df, x=\"Count\", y=\"Publication Type\", color=\"#003f5c\")\n",
        "plt.title(\"Reviews by Publication Type\")\n",
        "by_pubtype.set_yticklabels([\"Trade \\n Journal\", \"Newspaper/ \\n News Magazine\", \"Criticism\", \"Fan Magazine\"])\n",
        "by_pubtype.set(ylabel=None)\n",
        "by_pubtype.set_xlabel(\"Number of Reviews\")\n",
        "by_pubtype.bar_label(by_pubtype.containers[0], fmt=' %.0f')\n",
        "plt.savefig(\"N_PubType.jpg\", dpi=250, bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "f4988093-8a43-4bae-91f9-77a906a77f38",
        "id": "ZcLe-UQgfgKY"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FH3gKU4NY-JR"
      },
      "source": [
        "## Let's go over the human judgements"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### In the manual analysis, how many reviews are positive, mixed and negative overall?"
      ],
      "metadata": {
        "id": "9FWxh91a11hP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "judgement_counts = df['Manual_Judgment'].value_counts()\n",
        "judgement_percentages = df['Manual_Judgment'].value_counts(normalize=True)\n",
        "judgement_df = pd.DataFrame({'Count': judgement_counts, 'Percentage': judgement_percentages*100})\n",
        "judgement_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "F5YesPLYfZq8",
        "outputId": "5ac72136-8cc0-47a2-fddf-7aff4840bda0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Count  Percentage\n",
              "positive     44       55.00\n",
              "mixed        23       28.75\n",
              "negative     13       16.25"
            ],
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "\n",
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              "\n",
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              "    background-color: var(--bg-color);\n",
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              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a161b362-b675-4343-b166-9d6bb4a05060 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### How are these sentiments split by movie?"
      ],
      "metadata": {
        "id": "VdlnjL_vFVVY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Count the values for each of the sentiment columns, turn into a transposed dataframe so \"positive\", \"mixed\" and \"negative\" will be columns\n",
        "cali_judg = pd.DataFrame(df[(df['Movie'] == 'Caligari')]['Manual_Judgment'].value_counts()).T\n",
        "metro_judg = pd.DataFrame(df[(df['Movie'] == 'Metropolis')]['Manual_Judgment'].value_counts()).T\n",
        "nosf_judg = pd.DataFrame(df[(df['Movie'] == 'Nosferatu')]['Manual_Judgment'].value_counts()).T\n",
        "\n",
        "# Join the dataframes (opposite order than we want in the graph, because it puts things in ascending)\n",
        "judgement_counts = pd.concat([nosf_judg, metro_judg, cali_judg])\n",
        "# Assign new index values to the DataFrame\n",
        "judgement_counts.index = ['Nosferatu', 'Metropolis', 'Caligari']\n",
        "judgement_counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "52860099-1568-4cbf-81c0-a7b84c595b79",
        "id": "h6V4v8BeunTo"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            positive  mixed  negative\n",
              "Nosferatu          8      6         3\n",
              "Metropolis         7     13         5\n",
              "Caligari          29      4         5"
            ],
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              "  <tbody>\n",
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              "      <th>Nosferatu</th>\n",
              "      <td>8</td>\n",
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              "    </tr>\n",
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              "      <th>Caligari</th>\n",
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              "\n",
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              "\n",
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              "    border: 2px solid var(--fill-color);\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-60748d97-fea9-450d-bd00-53e837004c60 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data\n",
        "categories = judgement_counts.index\n",
        "positive_count = judgement_counts[\"positive\"]\n",
        "mixed_count = judgement_counts[\"mixed\"]\n",
        "negative_count = judgement_counts[\"negative\"]\n",
        "\n",
        "# Set plot size\n",
        "plt.figure(figsize=(6, 2))\n",
        "\n",
        "# Create the stacked horizontal bar chart\n",
        "positive_bars = plt.barh(categories, positive_count, color=\"darkgreen\")\n",
        "mixed_bars = plt.barh(categories, mixed_count, left=positive_count, color=\"gold\")\n",
        "negative_bars = plt.barh(categories, negative_count, left= positive_count + mixed_count, color=\"firebrick\")\n",
        "\n",
        "# Add labels to the bars\n",
        "for bar in positive_bars:\n",
        "    plt.text(bar.get_width()/2, bar.get_y() + bar.get_height()/2, f'{bar.get_width():.0f}', va='center', ha='center', color='white', fontsize=13, weight='bold')\n",
        "\n",
        "for bar, first_bar in zip(mixed_bars, positive_bars):\n",
        "    if bar.get_width() > 0:  # Check if the value is greater than 0\n",
        "        plt.text(first_bar.get_width() + bar.get_width() / 2, bar.get_y() + bar.get_height() / 2, f'{bar.get_width():.0f}', va='center', ha='center', color='black', fontsize=13, weight='bold')\n",
        "\n",
        "for bar, first_bar, second_bar in zip(negative_bars, mixed_bars, positive_bars):\n",
        "    plt.text(first_bar.get_width() + second_bar.get_width() + bar.get_width() / 2, bar.get_y() + bar.get_height() / 2, f'{bar.get_width():.0f}', va='center', ha='center', color='white', fontsize=13, weight='bold')\n",
        "\n",
        "# Add extra info\n",
        "plt.legend([\"Positive\", \"Mixed\", \"Negative\"], bbox_to_anchor=(1, 0.5))\n",
        "plt.ylabel(\"Movie\")\n",
        "plt.xlabel(\"Number of Reviews\")\n",
        "plt.title(\"Human Sentiment Analysis Counts\")\n",
        "# Save\n",
        "plt.savefig(\"Human_Sentiment_Counts.jpg\", dpi=250, bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "outputId": "6644e52c-25a9-455b-cd72-c3be8c58289d",
        "id": "AXwqDDI8unTp"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x200 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Machine-generated sentiment analysis\n",
        "Let's compare the human sentiments to the machine-generated ones"
      ],
      "metadata": {
        "id": "ihfwGj7QFrMR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Barchart all sentiment methods for all movies"
      ],
      "metadata": {
        "id": "kuW2qUeauiGP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Count the values for each of the sentiment columns, turn into a transposed dataframe so \"positive\", \"mixed\" and \"negative\" will be columns\n",
        "hum_judg = pd.DataFrame(df['Manual_Judgment'].value_counts()).T\n",
        "hum_binary = pd.DataFrame(df['Binary_Judgment'].value_counts()).T\n",
        "chat_judg = pd.DataFrame(df['VADER_ChatGPT_sentiment'].value_counts()).T\n",
        "NLTK_judg = pd.DataFrame(df['VADER_review_sentiment'].value_counts()).T\n",
        "\n",
        "# Join the dataframes and fill nas because there are no mixed reviews in a couple of them, as type integer\n",
        "judgement_counts = pd.concat([NLTK_judg, chat_judg, hum_binary, hum_judg]).fillna(0).astype(int)\n",
        "judgement_counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "iRekQn97qNCj",
        "outputId": "047e3389-6d83-4982-9fa6-2c8b1f26b519"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         positive  negative  mixed\n",
              "VADER_review_sentiment         66        14      0\n",
              "VADER_ChatGPT_sentiment        58        19      3\n",
              "Binary_Judgment                59        21      0\n",
              "Manual_Judgment                44        13     23"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-40b22203-5a01-40b6-b5d6-a54629d0a5b4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>positive</th>\n",
              "      <th>negative</th>\n",
              "      <th>mixed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>VADER_review_sentiment</th>\n",
              "      <td>66</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VADER_ChatGPT_sentiment</th>\n",
              "      <td>58</td>\n",
              "      <td>19</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Binary_Judgment</th>\n",
              "      <td>59</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Manual_Judgment</th>\n",
              "      <td>44</td>\n",
              "      <td>13</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data\n",
        "categories = judgement_counts.index\n",
        "positive_count = judgement_counts[\"positive\"]\n",
        "mixed_count = judgement_counts[\"mixed\"]\n",
        "negative_count = judgement_counts[\"negative\"]\n",
        "\n",
        "# Set plot size\n",
        "plt.figure(figsize=(8, 4))\n",
        "\n",
        "# Create the stacked horizontal bar chart\n",
        "positive_bars = plt.barh(categories, positive_count, color=\"darkgreen\")\n",
        "mixed_bars = plt.barh(categories, mixed_count, left=positive_count, color=\"gold\")\n",
        "negative_bars = plt.barh(categories, negative_count, left= positive_count + mixed_count, color=\"firebrick\")\n",
        "\n",
        "# Add labels to the bars\n",
        "for bar in positive_bars:\n",
        "    plt.text(bar.get_width()/2, bar.get_y() + bar.get_height()/2, f'{bar.get_width():.0f}', va='center', ha='center', color='white', fontsize=13, weight='bold')\n",
        "\n",
        "for bar, first_bar in zip(mixed_bars, positive_bars):\n",
        "    if bar.get_width() > 0:  # Check if the value is greater than 0\n",
        "        plt.text(first_bar.get_width() + bar.get_width() / 2, bar.get_y() + bar.get_height() / 2, f'{bar.get_width():.0f}', va='center', ha='center', color='black', fontsize=13, weight='bold')\n",
        "\n",
        "for bar, first_bar, second_bar in zip(negative_bars, mixed_bars, positive_bars):\n",
        "    plt.text(first_bar.get_width() + second_bar.get_width() + bar.get_width() / 2, bar.get_y() + bar.get_height() / 2, f'{bar.get_width():.0f}', va='center', ha='center', color='white', fontsize=13, weight='bold')\n",
        "\n",
        "# Add legend\n",
        "plt.legend([\"Positive\", \"Mixed\", \"Negative\"], bbox_to_anchor=(1, 0.5))\n",
        "\n",
        "# Set custom y-axis tick labels for clarity\n",
        "custom_labels = [\"VADER Analysis \\n of Reviews\", \"VADER Analysis \\n of ChatGPT Outputs\", \"Binary Manual Annotation \\n with ChatGPT Support\", \"Human-only \\n Manual Annotation\"]\n",
        "plt.yticks(range(len(custom_labels)), custom_labels)\n",
        "\n",
        "plt.ylabel(\"Method\", fontsize=16)\n",
        "plt.xlabel(\"Number of Reviews\", fontsize=16)\n",
        "plt.title(\"Sentiment Analysis Counts (All Movies)\", fontsize=22)\n",
        "plt.savefig(\"All_Sentiment_Counts.jpg\", dpi=250, bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "id": "LJIo8214ZxEv",
        "outputId": "8775e5e5-4a9d-4d85-fb63-a79c98f79e9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Barchart all sentiment methods Caligari"
      ],
      "metadata": {
        "id": "SL3XtvDhf61x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Select caligari reviews only\n",
        "caligari = df[(df['Movie'] == 'Caligari')]\n",
        "\n",
        "# Count the values for each of the sentiment columns, turn into a transposed dataframe so \"positive\", \"mixed\" and \"negative\" will be columns\n",
        "hum_judg = pd.DataFrame(caligari['Manual_Judgment'].value_counts()).T\n",
        "hum_binary = pd.DataFrame(caligari['Binary_Judgment'].value_counts()).T\n",
        "chat_judg = pd.DataFrame(caligari['VADER_ChatGPT_sentiment'].value_counts()).T\n",
        "NLTK_judg = pd.DataFrame(caligari['VADER_review_sentiment'].value_counts()).T\n",
        "\n",
        "# Join the dataframes and fill nas because there are no mixed reviews in a couple of them, as type integer\n",
        "judgement_counts = pd.concat([NLTK_judg, chat_judg, hum_binary, hum_judg]).fillna(0).astype(int)\n",
        "judgement_counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "49470b51-7247-4037-a333-6c85eaf67188",
        "id": "1m5zGCRyf4Xb"
      },
      "execution_count": null,
      "outputs": [
        {
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              "                         positive  negative  mixed\n",
              "VADER_review_sentiment         30         8      0\n",
              "VADER_ChatGPT_sentiment        31         6      1\n",
              "Binary_Judgment                32         6      0\n",
              "Manual_Judgment                29         5      4"
            ],
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          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data\n",
        "categories = judgement_counts.index\n",
        "positive_count = judgement_counts[\"positive\"]\n",
        "mixed_count = judgement_counts[\"mixed\"]\n",
        "negative_count = judgement_counts[\"negative\"]\n",
        "\n",
        "# Set plot size\n",
        "plt.figure(figsize=(8, 4))\n",
        "\n",
        "# Create the stacked horizontal bar chart\n",
        "positive_bars = plt.barh(categories, positive_count, color=\"darkgreen\")\n",
        "mixed_bars = plt.barh(categories, mixed_count, left=positive_count, color=\"gold\")\n",
        "negative_bars = plt.barh(categories, negative_count, left= positive_count + mixed_count, color=\"firebrick\")\n",
        "\n",
        "# Add labels to the bars\n",
        "for bar in positive_bars:\n",
        "    plt.text(bar.get_width()/2, bar.get_y() + bar.get_height()/2, f'{bar.get_width():.0f}', va='center', ha='center', color='white', fontsize=13, weight='bold')\n",
        "\n",
        "for bar, first_bar in zip(mixed_bars, positive_bars):\n",
        "    if bar.get_width() > 0:  # Check if the value is greater than 0\n",
        "        plt.text(first_bar.get_width() + bar.get_width() / 2, bar.get_y() + bar.get_height() / 2, f'{bar.get_width():.0f}', va='center', ha='center', color='black', fontsize=13, weight='bold')\n",
        "\n",
        "for bar, first_bar, second_bar in zip(negative_bars, mixed_bars, positive_bars):\n",
        "    plt.text(first_bar.get_width() + second_bar.get_width() + bar.get_width() / 2, bar.get_y() + bar.get_height() / 2, f'{bar.get_width():.0f}', va='center', ha='center', color='white', fontsize=13, weight='bold')\n",
        "\n",
        "# Add legend\n",
        "plt.legend([\"Positive\", \"Mixed\", \"Negative\"], bbox_to_anchor=(1, 0.5))\n",
        "\n",
        "# Set custom y-axis tick labels for clarity\n",
        "plt.yticks(range(len(custom_labels)), custom_labels)\n",
        "\n",
        "plt.ylabel(\"Method\", fontsize=16)\n",
        "plt.xlabel(\"Number of Reviews\", fontsize=16)\n",
        "plt.title(\"Sentiment Analysis Counts (Caligari)\", fontsize=22)\n",
        "plt.savefig(\"Caligari_Sentiment_Counts.jpg\", dpi=250, bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "outputId": "cffdbc12-1b8e-4bbc-f871-e2da14668109",
        "id": "-FF54a-mf4Xc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Barchart all sentiment methods Metropolis"
      ],
      "metadata": {
        "id": "uAwP44ZQhVWB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Select metropolis reviews only\n",
        "metropolis = df[(df['Movie'] == 'Metropolis')]\n",
        "\n",
        "# Count the values for each of the sentiment columns, turn into a transposed dataframe so \"positive\", \"mixed\" and \"negative\" will be columns\n",
        "hum_judg = pd.DataFrame(metropolis['Manual_Judgment'].value_counts()).T\n",
        "hum_binary = pd.DataFrame(metropolis['Binary_Judgment'].value_counts()).T\n",
        "chat_judg = pd.DataFrame(metropolis['VADER_ChatGPT_sentiment'].value_counts()).T\n",
        "NLTK_judg = pd.DataFrame(metropolis['VADER_review_sentiment'].value_counts()).T\n",
        "\n",
        "# Join the dataframes and fill nas because there are no mixed reviews in a couple of them, as type integer\n",
        "judgement_counts = pd.concat([NLTK_judg, chat_judg, hum_binary, hum_judg]).fillna(0).astype(int)\n",
        "judgement_counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "88c4b6da-ac10-4397-f1f1-5b0cdc4c5ef4",
        "id": "s2ilVawuhVWL"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         positive  negative  mixed\n",
              "VADER_review_sentiment         24         1      0\n",
              "VADER_ChatGPT_sentiment        15         8      2\n",
              "Binary_Judgment                13        12      0\n",
              "Manual_Judgment                 7         5     13"
            ],
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              "      <th></th>\n",
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              "  <tbody>\n",
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              "      <th>VADER_review_sentiment</th>\n",
              "      <td>24</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
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              "      <th>VADER_ChatGPT_sentiment</th>\n",
              "      <td>15</td>\n",
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              "      <th>Binary_Judgment</th>\n",
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              "      <td>12</td>\n",
              "      <td>0</td>\n",
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              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1c29dff1-1534-415b-bf64-56f88266119b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1c29dff1-1534-415b-bf64-56f88266119b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-7c056ba5-7e5c-44a2-a4e8-8a19bdb64cf7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7c056ba5-7e5c-44a2-a4e8-8a19bdb64cf7')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7c056ba5-7e5c-44a2-a4e8-8a19bdb64cf7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data\n",
        "categories = judgement_counts.index\n",
        "positive_count = judgement_counts[\"positive\"]\n",
        "mixed_count = judgement_counts[\"mixed\"]\n",
        "negative_count = judgement_counts[\"negative\"]\n",
        "\n",
        "# Set plot size\n",
        "plt.figure(figsize=(8, 4))\n",
        "\n",
        "# Create the stacked horizontal bar chart\n",
        "positive_bars = plt.barh(categories, positive_count, color=\"darkgreen\")\n",
        "mixed_bars = plt.barh(categories, mixed_count, left=positive_count, color=\"gold\")\n",
        "negative_bars = plt.barh(categories, negative_count, left= positive_count + mixed_count, color=\"firebrick\")\n",
        "\n",
        "# Add labels to the bars\n",
        "for bar in positive_bars:\n",
        "    plt.text(bar.get_width()/2, bar.get_y() + bar.get_height()/2, f'{bar.get_width():.0f}', va='center', ha='center', color='white', fontsize=13, weight='bold')\n",
        "\n",
        "for bar, first_bar in zip(mixed_bars, positive_bars):\n",
        "    if bar.get_width() > 0:  # Check if the value is greater than 0\n",
        "        plt.text(first_bar.get_width() + bar.get_width() / 2, bar.get_y() + bar.get_height() / 2, f'{bar.get_width():.0f}', va='center', ha='center', color='black', fontsize=13, weight='bold')\n",
        "\n",
        "for bar, first_bar, second_bar in zip(negative_bars, mixed_bars, positive_bars):\n",
        "    plt.text(first_bar.get_width() + second_bar.get_width() + bar.get_width() / 2, bar.get_y() + bar.get_height() / 2, f'{bar.get_width():.0f}', va='center', ha='center', color='white', fontsize=13, weight='bold')\n",
        "\n",
        "# Add legend\n",
        "plt.legend([\"Positive\", \"Mixed\", \"Negative\"], bbox_to_anchor=(1, 0.5))\n",
        "\n",
        "# Set custom y-axis tick labels for clarity\n",
        "plt.yticks(range(len(custom_labels)), custom_labels)\n",
        "\n",
        "plt.ylabel(\"Method\", fontsize=16)\n",
        "plt.xlabel(\"Number of Reviews\", fontsize=16)\n",
        "plt.title(\"Sentiment Analysis Counts (Metropolis)\", fontsize=22)\n",
        "plt.savefig(\"Metropolis_Sentiment_Counts.jpg\", dpi=250, bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "outputId": "fca7c276-b374-4b05-c5d1-6e83df6d947b",
        "id": "cBjI4oyrhVWM"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Barchart all sentiment methods Nosferatu"
      ],
      "metadata": {
        "id": "2-jLtTQTiLUb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Select nosferatu reviews only\n",
        "nosferatu = df[(df['Movie'] == 'Nosferatu')]\n",
        "\n",
        "# Count the values for each of the sentiment columns, turn into a transposed dataframe so \"positive\", \"mixed\" and \"negative\" will be columns\n",
        "hum_judg = pd.DataFrame(nosferatu['Manual_Judgment'].value_counts()).T\n",
        "hum_binary = pd.DataFrame(nosferatu['Binary_Judgment'].value_counts()).T\n",
        "chat_judg = pd.DataFrame(nosferatu['VADER_ChatGPT_sentiment'].value_counts()).T\n",
        "NLTK_judg = pd.DataFrame(nosferatu['VADER_review_sentiment'].value_counts()).T\n",
        "\n",
        "# Join the dataframes and fill nas because there are no mixed reviews in a couple of them, as type integer\n",
        "judgement_counts = pd.concat([NLTK_judg, chat_judg, hum_binary, hum_judg]).fillna(0).astype(int)\n",
        "judgement_counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "177aa7ce-b47a-400c-a525-167dbf1e2679",
        "id": "lGMedxEziLUm"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         positive  negative  mixed\n",
              "VADER_review_sentiment         12         5      0\n",
              "VADER_ChatGPT_sentiment        12         5      0\n",
              "Binary_Judgment                14         3      0\n",
              "Manual_Judgment                 8         3      6"
            ],
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              "      <th></th>\n",
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              "      <th>VADER_review_sentiment</th>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VADER_ChatGPT_sentiment</th>\n",
              "      <td>12</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
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              "      <th>Binary_Judgment</th>\n",
              "      <td>14</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-655cf1c9-c9ae-4b3f-b779-452f86ba84e4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data\n",
        "categories = judgement_counts.index\n",
        "positive_count = judgement_counts[\"positive\"]\n",
        "mixed_count = judgement_counts[\"mixed\"]\n",
        "negative_count = judgement_counts[\"negative\"]\n",
        "\n",
        "# Set plot size\n",
        "plt.figure(figsize=(8, 4))\n",
        "\n",
        "# Create the stacked horizontal bar chart\n",
        "positive_bars = plt.barh(categories, positive_count, color=\"darkgreen\")\n",
        "mixed_bars = plt.barh(categories, mixed_count, left=positive_count, color=\"gold\")\n",
        "negative_bars = plt.barh(categories, negative_count, left= positive_count + mixed_count, color=\"firebrick\")\n",
        "\n",
        "# Add labels to the bars\n",
        "for bar in positive_bars:\n",
        "    plt.text(bar.get_width()/2, bar.get_y() + bar.get_height()/2, f'{bar.get_width():.0f}', va='center', ha='center', color='white', fontsize=13, weight='bold')\n",
        "\n",
        "for bar, first_bar in zip(mixed_bars, positive_bars):\n",
        "    if bar.get_width() > 0:  # Check if the value is greater than 0\n",
        "        plt.text(first_bar.get_width() + bar.get_width() / 2, bar.get_y() + bar.get_height() / 2, f'{bar.get_width():.0f}', va='center', ha='center', color='black', fontsize=13, weight='bold')\n",
        "\n",
        "for bar, first_bar, second_bar in zip(negative_bars, mixed_bars, positive_bars):\n",
        "    plt.text(first_bar.get_width() + second_bar.get_width() + bar.get_width() / 2, bar.get_y() + bar.get_height() / 2, f'{bar.get_width():.0f}', va='center', ha='center', color='white', fontsize=13, weight='bold')\n",
        "\n",
        "# Add legend\n",
        "plt.legend([\"Positive\", \"Mixed\", \"Negative\"], bbox_to_anchor=(1, 0.5))\n",
        "\n",
        "# Set custom y-axis tick labels for clarity\n",
        "plt.yticks(range(len(custom_labels)), custom_labels)\n",
        "\n",
        "plt.ylabel(\"Method\", fontsize=16)\n",
        "plt.xlabel(\"Number of Reviews\", fontsize=16)\n",
        "plt.title(\"Sentiment Analysis Counts (Nosferatu)\", fontsize=22)\n",
        "plt.savefig(\"Nosferatu_Sentiment_Counts.jpg\", dpi=250, bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "outputId": "ea5a6a28-ce87-4108-83a2-d928306a4bf9",
        "id": "ZqWIiHs1iLUn"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Overall match\n",
        "What's the percentage of ChatGPT sentiment analyses that matches the original human sentiment - either the binary or the original?"
      ],
      "metadata": {
        "id": "D6Fu8jFVwa2E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's select just the columns we want\n",
        "sentiment = df[['Movie','Manual_Judgment','Binary_Judgment', 'VADER_ChatGPT_sentiment','VADER_ChatGPT_score','VADER_review_sentiment','VADER_review_score']]\n",
        "\n",
        "# Create a new column 'Check_GPT' to check if column 'VADER_ChatGPT_sentiment' has the same content as 'Manual_Judgment' or 'Binary_Judgment'\n",
        "sentiment['Check_GPT'] = sentiment.apply(lambda row: 'Match' if row['VADER_ChatGPT_sentiment'] == row['Manual_Judgment'] or\n",
        "                                         row['VADER_ChatGPT_sentiment'] == row['Binary_Judgment'] else 'No Match', axis=1)\n",
        "\n",
        "# Create a new column 'Check_VADER' to check if column 'VADER_review_sentiment' has the same content as 'Manual_Judgment' or 'Binary_Judgment'\n",
        "sentiment['Check_VADER'] = sentiment.apply(lambda row: 'Match' if row['VADER_review_sentiment'] == row['Manual_Judgment'] or\n",
        "                                         row['VADER_review_sentiment'] == row['Binary_Judgment'] else 'No Match', axis=1)\n",
        "\n",
        "sentiment"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "O8yz_4SpmPC9",
        "outputId": "9c042dd3-1e1e-4557-b773-0e5afe6a5319"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-26-2177c624c85b>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  sentiment['Check_GPT'] = sentiment.apply(lambda row: 'Match' if row['VADER_ChatGPT_sentiment'] == row['Manual_Judgment'] or\n",
            "<ipython-input-26-2177c624c85b>:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  sentiment['Check_VADER'] = sentiment.apply(lambda row: 'Match' if row['VADER_review_sentiment'] == row['Manual_Judgment'] or\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Movie Manual_Judgment Binary_Judgment VADER_ChatGPT_sentiment  \\\n",
              "ID                                                                            \n",
              "2MBUUSRR   Caligari        positive        positive                positive   \n",
              "MXZKVQ6B   Caligari        negative        negative                negative   \n",
              "DPRVUI8Q   Caligari        positive        positive                positive   \n",
              "G2XQWE7E   Caligari        positive        positive                positive   \n",
              "LU2I7PCZ   Caligari        negative        negative                negative   \n",
              "...             ...             ...             ...                     ...   \n",
              "2SQ2YDPA  Nosferatu           mixed        positive                positive   \n",
              "KWC9KUTP  Nosferatu        negative        negative                negative   \n",
              "LDB96ADR  Nosferatu        positive        positive                positive   \n",
              "SBML59AF  Nosferatu        positive        positive                positive   \n",
              "L7JBS7LN  Nosferatu        positive        positive                positive   \n",
              "\n",
              "          VADER_ChatGPT_score VADER_review_sentiment  VADER_review_score  \\\n",
              "ID                                                                         \n",
              "2MBUUSRR               0.9750               positive              0.2960   \n",
              "MXZKVQ6B              -0.6103               positive              0.9975   \n",
              "DPRVUI8Q               0.9856               positive              0.9970   \n",
              "G2XQWE7E               0.9842               negative             -0.5387   \n",
              "LU2I7PCZ              -0.9169               positive              0.2709   \n",
              "...                       ...                    ...                 ...   \n",
              "2SQ2YDPA               0.7394               negative             -0.9802   \n",
              "KWC9KUTP              -0.7555               positive              0.9941   \n",
              "LDB96ADR               0.9451               positive              0.9872   \n",
              "SBML59AF               0.9169               positive              0.3400   \n",
              "L7JBS7LN               0.9433               negative             -0.9692   \n",
              "\n",
              "         Check_GPT Check_VADER  \n",
              "ID                              \n",
              "2MBUUSRR     Match       Match  \n",
              "MXZKVQ6B     Match    No Match  \n",
              "DPRVUI8Q     Match       Match  \n",
              "G2XQWE7E     Match    No Match  \n",
              "LU2I7PCZ     Match    No Match  \n",
              "...            ...         ...  \n",
              "2SQ2YDPA     Match    No Match  \n",
              "KWC9KUTP     Match    No Match  \n",
              "LDB96ADR     Match       Match  \n",
              "SBML59AF     Match       Match  \n",
              "L7JBS7LN     Match    No Match  \n",
              "\n",
              "[80 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4f0f8432-9cb6-4638-bde5-24f097256c3e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Movie</th>\n",
              "      <th>Manual_Judgment</th>\n",
              "      <th>Binary_Judgment</th>\n",
              "      <th>VADER_ChatGPT_sentiment</th>\n",
              "      <th>VADER_ChatGPT_score</th>\n",
              "      <th>VADER_review_sentiment</th>\n",
              "      <th>VADER_review_score</th>\n",
              "      <th>Check_GPT</th>\n",
              "      <th>Check_VADER</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2MBUUSRR</th>\n",
              "      <td>Caligari</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.9750</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.2960</td>\n",
              "      <td>Match</td>\n",
              "      <td>Match</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MXZKVQ6B</th>\n",
              "      <td>Caligari</td>\n",
              "      <td>negative</td>\n",
              "      <td>negative</td>\n",
              "      <td>negative</td>\n",
              "      <td>-0.6103</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.9975</td>\n",
              "      <td>Match</td>\n",
              "      <td>No Match</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DPRVUI8Q</th>\n",
              "      <td>Caligari</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.9856</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>Match</td>\n",
              "      <td>Match</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>G2XQWE7E</th>\n",
              "      <td>Caligari</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.9842</td>\n",
              "      <td>negative</td>\n",
              "      <td>-0.5387</td>\n",
              "      <td>Match</td>\n",
              "      <td>No Match</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LU2I7PCZ</th>\n",
              "      <td>Caligari</td>\n",
              "      <td>negative</td>\n",
              "      <td>negative</td>\n",
              "      <td>negative</td>\n",
              "      <td>-0.9169</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.2709</td>\n",
              "      <td>Match</td>\n",
              "      <td>No Match</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2SQ2YDPA</th>\n",
              "      <td>Nosferatu</td>\n",
              "      <td>mixed</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.7394</td>\n",
              "      <td>negative</td>\n",
              "      <td>-0.9802</td>\n",
              "      <td>Match</td>\n",
              "      <td>No Match</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KWC9KUTP</th>\n",
              "      <td>Nosferatu</td>\n",
              "      <td>negative</td>\n",
              "      <td>negative</td>\n",
              "      <td>negative</td>\n",
              "      <td>-0.7555</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.9941</td>\n",
              "      <td>Match</td>\n",
              "      <td>No Match</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LDB96ADR</th>\n",
              "      <td>Nosferatu</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.9451</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.9872</td>\n",
              "      <td>Match</td>\n",
              "      <td>Match</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SBML59AF</th>\n",
              "      <td>Nosferatu</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.9169</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.3400</td>\n",
              "      <td>Match</td>\n",
              "      <td>Match</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>L7JBS7LN</th>\n",
              "      <td>Nosferatu</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.9433</td>\n",
              "      <td>negative</td>\n",
              "      <td>-0.9692</td>\n",
              "      <td>Match</td>\n",
              "      <td>No Match</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>80 rows × 9 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4f0f8432-9cb6-4638-bde5-24f097256c3e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4f0f8432-9cb6-4638-bde5-24f097256c3e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4f0f8432-9cb6-4638-bde5-24f097256c3e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6ccf7ab0-ab12-43fd-bb4e-d3fb9f7dcf8b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6ccf7ab0-ab12-43fd-bb4e-d3fb9f7dcf8b')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6ccf7ab0-ab12-43fd-bb4e-d3fb9f7dcf8b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ChatGPT vs VADER Human Sentiment Check"
      ],
      "metadata": {
        "id": "Cgy130RekpJ6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Count the values for the check columns, turn into a transposed dataframe so \"Match\" and \"No Match\" will be columns\n",
        "check_gpt = pd.DataFrame(sentiment[\"Check_GPT\"].value_counts()).T\n",
        "check_vader = pd.DataFrame(sentiment[\"Check_VADER\"].value_counts()).T\n",
        "\n",
        "# Join the dataframes\n",
        "check_counts = pd.concat([check_vader, check_gpt])\n",
        "check_counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "O5w_f6XSqQ8J",
        "outputId": "5d349eb9-7fb4-4c4c-a571-17143007856e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Match  No Match\n",
              "Check_VADER     53        27\n",
              "Check_GPT       71         9"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8fad0c0b-bb4c-40a9-bb23-eed5ec36967c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Match</th>\n",
              "      <th>No Match</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Check_VADER</th>\n",
              "      <td>53</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Check_GPT</th>\n",
              "      <td>71</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8fad0c0b-bb4c-40a9-bb23-eed5ec36967c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8fad0c0b-bb4c-40a9-bb23-eed5ec36967c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d1097958-20bd-46a5-9b20-9ae6207b8c81\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d1097958-20bd-46a5-9b20-9ae6207b8c81')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d1097958-20bd-46a5-9b20-9ae6207b8c81 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data\n",
        "categories = check_counts.index\n",
        "true_count = check_counts[\"Match\"]\n",
        "false_count = check_counts[\"No Match\"]\n",
        "\n",
        "# Set plot size\n",
        "plt.figure(figsize=(6, 2))\n",
        "\n",
        "# Create the stacked horizontal bar chart\n",
        "true_bars = plt.barh(categories, true_count, color=\"darkgreen\")\n",
        "false_bars = plt.barh(categories, false_count, left=true_count, color=\"firebrick\")\n",
        "\n",
        "# Add labels to the bars\n",
        "for bar in true_bars:\n",
        "    plt.text(bar.get_width()/2, bar.get_y() + bar.get_height()/2, f'{bar.get_width():.0f}', va='center', ha='center', color='white', fontsize=13, weight='bold')\n",
        "\n",
        "for bar, first_bar in zip(false_bars, true_bars):\n",
        "    if bar.get_width() > 0:  # Check if the value is greater than 0\n",
        "        plt.text(first_bar.get_width() + bar.get_width() / 2, bar.get_y() + bar.get_height() / 2, f'{bar.get_width():.0f}', va='center', ha='center', color='white', fontsize=13, weight='bold')\n",
        "\n",
        "\n",
        "# Add legend\n",
        "plt.legend([\"Match\", \"No Match\"], bbox_to_anchor=(1, 0.5))\n",
        "\n",
        "# Set custom y-axis tick labels for clarity\n",
        "custom_labels = [\"VADER only\", \"VADER-ChatGPT\"]\n",
        "plt.yticks(range(len(custom_labels)), custom_labels)\n",
        "\n",
        "plt.ylabel(\"Method\", fontsize=16)\n",
        "plt.xlabel(\"Number of Reviews\", fontsize=16)\n",
        "plt.title(\"Match of Automated Methods vs. Manual Annotation\", fontsize=18)\n",
        "plt.savefig(\"ChatGPT_vs_NLTK_HumanCheck.jpg\", dpi=250, bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        },
        "id": "4rsChhhGmQRk",
        "outputId": "0c4c5ac4-837a-4e63-ffbf-16778aa3f181"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x200 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can already see that there is a big improvament in the reliability of the sentiment analysis intermediating through ChatGPT compared to directly through VADER."
      ],
      "metadata": {
        "id": "kqDh4kzrcGCf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Let's do a check of in what direction things are wrong"
      ],
      "metadata": {
        "id": "3OFZT2mZkAf7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove check columns because they'll be redundant here\n",
        "sentiment.drop(columns=[\"Check_GPT\", \"Check_VADER\"], inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p1zZ2JMLx6SU",
        "outputId": "532b9c52-61fc-422e-b34a-b9f97e1d40d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-29-81217d91eb79>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  sentiment.drop(columns=[\"Check_GPT\", \"Check_VADER\"], inplace=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to determine in which direction things were wrong\n",
        "def wrong_because(row, column_to_check):\n",
        "    if row[column_to_check] == row['Manual_Judgment'] or row[column_to_check] == row['Binary_Judgment']:\n",
        "        return 'Correct'\n",
        "    elif row['Binary_Judgment'] == 'negative' and row[column_to_check] == 'positive':\n",
        "        return 'Falsely Positive'\n",
        "    elif row['Binary_Judgment'] == 'positive' and row[column_to_check] == 'negative':\n",
        "        return 'Falsely Negative'\n",
        "    else:\n",
        "        return None\n",
        "\n",
        "# Apply the function to create the new columns\n",
        "sentiment['GPT_Error_Check'] = sentiment.apply(lambda row: wrong_because(row, 'VADER_ChatGPT_sentiment'), axis=1)\n",
        "sentiment['VADER_Error_Check'] = sentiment.apply(lambda row: wrong_because(row, 'VADER_review_sentiment'), axis=1)"
      ],
      "metadata": {
        "id": "vqD8NE-HkCB6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f29e076-abb5-4db2-8e89-fb703c4f343d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-30-e886a11d3f36>:13: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  sentiment['GPT_Error_Check'] = sentiment.apply(lambda row: wrong_because(row, 'VADER_ChatGPT_sentiment'), axis=1)\n",
            "<ipython-input-30-e886a11d3f36>:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  sentiment['VADER_Error_Check'] = sentiment.apply(lambda row: wrong_because(row, 'VADER_review_sentiment'), axis=1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sentiment"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "hISsEMUg6hts",
        "outputId": "5baa8310-13a5-46c4-f2ac-098a79bdaef0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Movie Manual_Judgment Binary_Judgment VADER_ChatGPT_sentiment  \\\n",
              "ID                                                                            \n",
              "2MBUUSRR   Caligari        positive        positive                positive   \n",
              "MXZKVQ6B   Caligari        negative        negative                negative   \n",
              "DPRVUI8Q   Caligari        positive        positive                positive   \n",
              "G2XQWE7E   Caligari        positive        positive                positive   \n",
              "LU2I7PCZ   Caligari        negative        negative                negative   \n",
              "...             ...             ...             ...                     ...   \n",
              "2SQ2YDPA  Nosferatu           mixed        positive                positive   \n",
              "KWC9KUTP  Nosferatu        negative        negative                negative   \n",
              "LDB96ADR  Nosferatu        positive        positive                positive   \n",
              "SBML59AF  Nosferatu        positive        positive                positive   \n",
              "L7JBS7LN  Nosferatu        positive        positive                positive   \n",
              "\n",
              "          VADER_ChatGPT_score VADER_review_sentiment  VADER_review_score  \\\n",
              "ID                                                                         \n",
              "2MBUUSRR               0.9750               positive              0.2960   \n",
              "MXZKVQ6B              -0.6103               positive              0.9975   \n",
              "DPRVUI8Q               0.9856               positive              0.9970   \n",
              "G2XQWE7E               0.9842               negative             -0.5387   \n",
              "LU2I7PCZ              -0.9169               positive              0.2709   \n",
              "...                       ...                    ...                 ...   \n",
              "2SQ2YDPA               0.7394               negative             -0.9802   \n",
              "KWC9KUTP              -0.7555               positive              0.9941   \n",
              "LDB96ADR               0.9451               positive              0.9872   \n",
              "SBML59AF               0.9169               positive              0.3400   \n",
              "L7JBS7LN               0.9433               negative             -0.9692   \n",
              "\n",
              "         GPT_Error_Check VADER_Error_Check  \n",
              "ID                                          \n",
              "2MBUUSRR         Correct           Correct  \n",
              "MXZKVQ6B         Correct  Falsely Positive  \n",
              "DPRVUI8Q         Correct           Correct  \n",
              "G2XQWE7E         Correct  Falsely Negative  \n",
              "LU2I7PCZ         Correct  Falsely Positive  \n",
              "...                  ...               ...  \n",
              "2SQ2YDPA         Correct  Falsely Negative  \n",
              "KWC9KUTP         Correct  Falsely Positive  \n",
              "LDB96ADR         Correct           Correct  \n",
              "SBML59AF         Correct           Correct  \n",
              "L7JBS7LN         Correct  Falsely Negative  \n",
              "\n",
              "[80 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b1e9638c-20bb-49a4-8fa5-f86472a4ee02\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Movie</th>\n",
              "      <th>Manual_Judgment</th>\n",
              "      <th>Binary_Judgment</th>\n",
              "      <th>VADER_ChatGPT_sentiment</th>\n",
              "      <th>VADER_ChatGPT_score</th>\n",
              "      <th>VADER_review_sentiment</th>\n",
              "      <th>VADER_review_score</th>\n",
              "      <th>GPT_Error_Check</th>\n",
              "      <th>VADER_Error_Check</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2MBUUSRR</th>\n",
              "      <td>Caligari</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.9750</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.2960</td>\n",
              "      <td>Correct</td>\n",
              "      <td>Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MXZKVQ6B</th>\n",
              "      <td>Caligari</td>\n",
              "      <td>negative</td>\n",
              "      <td>negative</td>\n",
              "      <td>negative</td>\n",
              "      <td>-0.6103</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.9975</td>\n",
              "      <td>Correct</td>\n",
              "      <td>Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DPRVUI8Q</th>\n",
              "      <td>Caligari</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.9856</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>Correct</td>\n",
              "      <td>Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>G2XQWE7E</th>\n",
              "      <td>Caligari</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.9842</td>\n",
              "      <td>negative</td>\n",
              "      <td>-0.5387</td>\n",
              "      <td>Correct</td>\n",
              "      <td>Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LU2I7PCZ</th>\n",
              "      <td>Caligari</td>\n",
              "      <td>negative</td>\n",
              "      <td>negative</td>\n",
              "      <td>negative</td>\n",
              "      <td>-0.9169</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.2709</td>\n",
              "      <td>Correct</td>\n",
              "      <td>Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2SQ2YDPA</th>\n",
              "      <td>Nosferatu</td>\n",
              "      <td>mixed</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.7394</td>\n",
              "      <td>negative</td>\n",
              "      <td>-0.9802</td>\n",
              "      <td>Correct</td>\n",
              "      <td>Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KWC9KUTP</th>\n",
              "      <td>Nosferatu</td>\n",
              "      <td>negative</td>\n",
              "      <td>negative</td>\n",
              "      <td>negative</td>\n",
              "      <td>-0.7555</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.9941</td>\n",
              "      <td>Correct</td>\n",
              "      <td>Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LDB96ADR</th>\n",
              "      <td>Nosferatu</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.9451</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.9872</td>\n",
              "      <td>Correct</td>\n",
              "      <td>Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SBML59AF</th>\n",
              "      <td>Nosferatu</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.9169</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.3400</td>\n",
              "      <td>Correct</td>\n",
              "      <td>Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>L7JBS7LN</th>\n",
              "      <td>Nosferatu</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>positive</td>\n",
              "      <td>0.9433</td>\n",
              "      <td>negative</td>\n",
              "      <td>-0.9692</td>\n",
              "      <td>Correct</td>\n",
              "      <td>Falsely Negative</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>80 rows × 9 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b1e9638c-20bb-49a4-8fa5-f86472a4ee02 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b1e9638c-20bb-49a4-8fa5-f86472a4ee02');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-56ba95cf-cec5-4e50-a68b-c74c3c798f19')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-56ba95cf-cec5-4e50-a68b-c74c3c798f19 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Sentiment heat table"
      ],
      "metadata": {
        "id": "SyXq63OgwSvC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "It would be helpful to color code this so it's easier to instinctively understand."
      ],
      "metadata": {
        "id": "48Ooy4536o-c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the color map for numerical values\n",
        "cmapR = 'RdYlGn'\n",
        "\n",
        "# Define a function to map sentiment text to colors\n",
        "def map_sentiment_to_color(text):\n",
        "    if text == 'positive':\n",
        "        return 'background-color: darkgreen; color: white'\n",
        "    elif text == 'mixed':\n",
        "        return 'background-color: gold'\n",
        "    elif text == 'negative':\n",
        "        return 'background-color: firebrick; color: white'\n",
        "\n",
        "# Define a function to map errors to colors\n",
        "def map_errors_to_color(text):\n",
        "    if text == 'Correct':\n",
        "        return 'background-color: darkgreen; color: white'\n",
        "    elif text == 'Falsely Positive':\n",
        "        return 'background-color: white; color: darkgreen'\n",
        "    elif text == 'Falsely Negative':\n",
        "        return 'background-color: white; color: firebrick'\n",
        "\n",
        "# Apply the custom function to style the DataFrame\n",
        "sentiment_map = sentiment.style.applymap(map_sentiment_to_color, subset=['Manual_Judgment', 'Binary_Judgment', 'VADER_review_sentiment', 'VADER_ChatGPT_sentiment'])\\\n",
        "    .set_properties(**{\n",
        "        'text-align': 'center',\n",
        "        'font-size': '16px'  # You can adjust the font size as desired\n",
        "    })\\\n",
        "    .background_gradient(cmap=cmapR, subset=['VADER_review_score', 'VADER_ChatGPT_score'], vmin=-1, vmax=1)\\\n",
        "    .applymap(map_errors_to_color, subset=['GPT_Error_Check', 'VADER_Error_Check'])  # Apply map_match_to_color to specific columns\n",
        "\n",
        "# Save the styled DataFrame to an HTML file\n",
        "sentiment_map.to_html('sentiment_heatmap_all.html')"
      ],
      "metadata": {
        "id": "ERY3cCfjBoIl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sentiment_map"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "jy-I_DCnL9nc",
        "outputId": "600e3609-2bb9-4847-d03b-dbf13a5d1e7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7c7c2d215810>"
            ],
            "text/html": [
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              "  background-color: darkgreen;\n",
              "  color: white;\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "}\n",
              "#T_f79c4_row0_col4, #T_f79c4_row9_col4, #T_f79c4_row14_col4, #T_f79c4_row17_col6, #T_f79c4_row24_col4, #T_f79c4_row30_col4, #T_f79c4_row54_col6, #T_f79c4_row58_col6, #T_f79c4_row60_col4, #T_f79c4_row69_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #036e3a;\n",
              "  color: #f1f1f1;\n",
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              "#T_f79c4_row0_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #c1e57b;\n",
              "  color: #000000;\n",
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              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: darkgreen;\n",
              "  color: white;\n",
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              "#T_f79c4_row1_col1, #T_f79c4_row1_col2, #T_f79c4_row1_col3, #T_f79c4_row3_col5, #T_f79c4_row4_col1, #T_f79c4_row4_col2, #T_f79c4_row4_col3, #T_f79c4_row9_col1, #T_f79c4_row9_col2, #T_f79c4_row12_col5, #T_f79c4_row13_col1, #T_f79c4_row13_col2, #T_f79c4_row13_col3, #T_f79c4_row16_col5, #T_f79c4_row18_col1, #T_f79c4_row18_col2, #T_f79c4_row18_col3, #T_f79c4_row18_col5, #T_f79c4_row22_col2, #T_f79c4_row22_col3, #T_f79c4_row22_col5, #T_f79c4_row29_col3, #T_f79c4_row29_col5, #T_f79c4_row30_col5, #T_f79c4_row37_col5, #T_f79c4_row38_col2, #T_f79c4_row40_col2, #T_f79c4_row41_col2, #T_f79c4_row41_col3, #T_f79c4_row43_col1, #T_f79c4_row43_col2, #T_f79c4_row43_col3, #T_f79c4_row46_col2, #T_f79c4_row47_col1, #T_f79c4_row47_col2, #T_f79c4_row47_col3, #T_f79c4_row47_col5, #T_f79c4_row50_col2, #T_f79c4_row52_col1, #T_f79c4_row52_col2, #T_f79c4_row52_col3, #T_f79c4_row53_col1, #T_f79c4_row53_col2, #T_f79c4_row53_col3, #T_f79c4_row56_col2, #T_f79c4_row56_col3, #T_f79c4_row57_col1, #T_f79c4_row57_col2, #T_f79c4_row61_col3, #T_f79c4_row62_col2, #T_f79c4_row62_col3, #T_f79c4_row63_col1, #T_f79c4_row63_col2, #T_f79c4_row63_col3, #T_f79c4_row64_col3, #T_f79c4_row65_col1, #T_f79c4_row65_col2, #T_f79c4_row65_col3, #T_f79c4_row65_col5, #T_f79c4_row66_col5, #T_f79c4_row67_col5, #T_f79c4_row74_col3, #T_f79c4_row75_col5, #T_f79c4_row76_col1, #T_f79c4_row76_col2, #T_f79c4_row76_col3, #T_f79c4_row79_col5 {\n",
              "  background-color: firebrick;\n",
              "  color: white;\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "}\n",
              "#T_f79c4_row1_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #f26841;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row1_col6, #T_f79c4_row2_col6, #T_f79c4_row14_col6, #T_f79c4_row26_col6, #T_f79c4_row33_col4, #T_f79c4_row33_col6, #T_f79c4_row34_col6, #T_f79c4_row35_col6, #T_f79c4_row36_col6, #T_f79c4_row40_col6, #T_f79c4_row42_col6, #T_f79c4_row43_col6, #T_f79c4_row44_col6, #T_f79c4_row45_col6, #T_f79c4_row49_col6, #T_f79c4_row50_col6, #T_f79c4_row51_col6, #T_f79c4_row53_col6, #T_f79c4_row56_col6, #T_f79c4_row57_col6, #T_f79c4_row60_col6, #T_f79c4_row61_col6, #T_f79c4_row62_col6, #T_f79c4_row71_col6, #T_f79c4_row73_col6, #T_f79c4_row76_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #006837;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row1_col8, #T_f79c4_row4_col8, #T_f79c4_row9_col7, #T_f79c4_row9_col8, #T_f79c4_row13_col8, #T_f79c4_row38_col7, #T_f79c4_row38_col8, #T_f79c4_row40_col7, #T_f79c4_row40_col8, #T_f79c4_row41_col8, #T_f79c4_row43_col8, #T_f79c4_row46_col8, #T_f79c4_row50_col7, #T_f79c4_row50_col8, #T_f79c4_row52_col8, #T_f79c4_row53_col8, #T_f79c4_row56_col8, #T_f79c4_row57_col7, #T_f79c4_row57_col8, #T_f79c4_row62_col8, #T_f79c4_row63_col8, #T_f79c4_row76_col8 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: white;\n",
              "  color: darkgreen;\n",
              "}\n",
              "#T_f79c4_row2_col4, #T_f79c4_row5_col4, #T_f79c4_row8_col4, #T_f79c4_row8_col6, #T_f79c4_row10_col6, #T_f79c4_row20_col4, #T_f79c4_row20_col6, #T_f79c4_row32_col6, #T_f79c4_row39_col6, #T_f79c4_row41_col6, #T_f79c4_row44_col4, #T_f79c4_row68_col6, #T_f79c4_row70_col4, #T_f79c4_row77_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #016a38;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row3_col4, #T_f79c4_row7_col4, #T_f79c4_row23_col6, #T_f79c4_row24_col6, #T_f79c4_row28_col4, #T_f79c4_row31_col6, #T_f79c4_row32_col4, #T_f79c4_row34_col4, #T_f79c4_row38_col6, #T_f79c4_row42_col4, #T_f79c4_row45_col4, #T_f79c4_row49_col4, #T_f79c4_row51_col4, #T_f79c4_row57_col4, #T_f79c4_row72_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #026c39;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row3_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #f7814c;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row3_col8, #T_f79c4_row12_col8, #T_f79c4_row16_col8, #T_f79c4_row29_col7, #T_f79c4_row29_col8, #T_f79c4_row30_col8, #T_f79c4_row37_col8, #T_f79c4_row61_col7, #T_f79c4_row64_col7, #T_f79c4_row66_col8, #T_f79c4_row67_col8, #T_f79c4_row74_col7, #T_f79c4_row75_col8, #T_f79c4_row79_col8 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: white;\n",
              "  color: firebrick;\n",
              "}\n",
              "#T_f79c4_row4_col4, #T_f79c4_row53_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #b91326;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row4_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #c7e77f;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_f79c4_row5_col6, #T_f79c4_row7_col6, #T_f79c4_row52_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #0c7f43;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row6_col4, #T_f79c4_row16_col4, #T_f79c4_row58_col4, #T_f79c4_row72_col4, #T_f79c4_row78_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #0a7b41;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row6_col6, #T_f79c4_row10_col4, #T_f79c4_row77_col4, #T_f79c4_row79_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #07753e;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row9_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #96d268;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_f79c4_row11_col4, #T_f79c4_row21_col6, #T_f79c4_row25_col6, #T_f79c4_row27_col6, #T_f79c4_row67_col4, #T_f79c4_row73_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #08773f;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row11_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #7dc765;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_f79c4_row12_col1, #T_f79c4_row15_col1, #T_f79c4_row21_col1, #T_f79c4_row21_col3, #T_f79c4_row22_col1, #T_f79c4_row38_col1, #T_f79c4_row39_col1, #T_f79c4_row40_col1, #T_f79c4_row41_col1, #T_f79c4_row42_col1, #T_f79c4_row46_col1, #T_f79c4_row46_col3, #T_f79c4_row50_col1, #T_f79c4_row55_col1, #T_f79c4_row56_col1, #T_f79c4_row59_col1, #T_f79c4_row59_col3, #T_f79c4_row60_col1, #T_f79c4_row61_col1, #T_f79c4_row62_col1, #T_f79c4_row64_col1, #T_f79c4_row66_col1, #T_f79c4_row67_col1, #T_f79c4_row73_col1, #T_f79c4_row74_col1, #T_f79c4_row75_col1 {\n",
              "  background-color: gold;\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "}\n",
              "#T_f79c4_row12_col4, #T_f79c4_row19_col4, #T_f79c4_row36_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #05713c;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row12_col6, #T_f79c4_row22_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #ad0826;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row13_col4, #T_f79c4_row65_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #e65036;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row13_col6, #T_f79c4_row28_col6, #T_f79c4_row55_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #0e8245;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row15_col4, #T_f79c4_row15_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #0d8044;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row16_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #c62027;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row17_col4, #T_f79c4_row70_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #128a49;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row18_col4, #T_f79c4_row64_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #c21c27;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row18_col6, #T_f79c4_row67_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #be1827;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row19_col6, #T_f79c4_row48_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #148e4b;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row21_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #feeb9d;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_f79c4_row22_col6, #T_f79c4_row29_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #a70226;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row23_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #b3df72;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_f79c4_row25_col4, #T_f79c4_row26_col4, #T_f79c4_row39_col4, #T_f79c4_row54_col4, #T_f79c4_row59_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #06733d;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row27_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #33a456;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row29_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #fdb768;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_f79c4_row30_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #f57245;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row31_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #199750;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row35_col4, #T_f79c4_row46_col6, #T_f79c4_row66_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #097940;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row37_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #7ac665;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_f79c4_row37_col6, #T_f79c4_row66_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #a50026;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row38_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #84ca66;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_f79c4_row40_col4, #T_f79c4_row50_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #7fc866;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_f79c4_row41_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #de402e;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row43_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #af0926;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row46_col4, #T_f79c4_row59_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #fff5ae;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_f79c4_row47_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #b10b26;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row47_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #fdaf62;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_f79c4_row48_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #0b7d42;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row52_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #ce2827;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row55_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #69be63;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row56_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #bd1726;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row61_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #e54e35;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row62_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #c41e27;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row63_col4, #T_f79c4_row79_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #ab0626;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row63_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #108647;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row64_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #4bb05c;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row65_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #e24731;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row68_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #1b9950;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row69_col6, #T_f79c4_row71_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #04703b;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row74_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #bb1526;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row74_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #6bbf64;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_f79c4_row75_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #30a356;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row75_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #a90426;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row76_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #dd3d2d;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_f79c4_row78_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #b5df74;\n",
              "  color: #000000;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_f79c4\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_f79c4_level0_col0\" class=\"col_heading level0 col0\" >Movie</th>\n",
              "      <th id=\"T_f79c4_level0_col1\" class=\"col_heading level0 col1\" >Manual_Judgment</th>\n",
              "      <th id=\"T_f79c4_level0_col2\" class=\"col_heading level0 col2\" >Binary_Judgment</th>\n",
              "      <th id=\"T_f79c4_level0_col3\" class=\"col_heading level0 col3\" >VADER_ChatGPT_sentiment</th>\n",
              "      <th id=\"T_f79c4_level0_col4\" class=\"col_heading level0 col4\" >VADER_ChatGPT_score</th>\n",
              "      <th id=\"T_f79c4_level0_col5\" class=\"col_heading level0 col5\" >VADER_review_sentiment</th>\n",
              "      <th id=\"T_f79c4_level0_col6\" class=\"col_heading level0 col6\" >VADER_review_score</th>\n",
              "      <th id=\"T_f79c4_level0_col7\" class=\"col_heading level0 col7\" >GPT_Error_Check</th>\n",
              "      <th id=\"T_f79c4_level0_col8\" class=\"col_heading level0 col8\" >VADER_Error_Check</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >ID</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "      <th class=\"blank col6\" >&nbsp;</th>\n",
              "      <th class=\"blank col7\" >&nbsp;</th>\n",
              "      <th class=\"blank col8\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row0\" class=\"row_heading level0 row0\" >2MBUUSRR</th>\n",
              "      <td id=\"T_f79c4_row0_col0\" class=\"data row0 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row0_col1\" class=\"data row0 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row0_col2\" class=\"data row0 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row0_col3\" class=\"data row0 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row0_col4\" class=\"data row0 col4\" >0.975000</td>\n",
              "      <td id=\"T_f79c4_row0_col5\" class=\"data row0 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row0_col6\" class=\"data row0 col6\" >0.296000</td>\n",
              "      <td id=\"T_f79c4_row0_col7\" class=\"data row0 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row0_col8\" class=\"data row0 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row1\" class=\"row_heading level0 row1\" >MXZKVQ6B</th>\n",
              "      <td id=\"T_f79c4_row1_col0\" class=\"data row1 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row1_col1\" class=\"data row1 col1\" >negative</td>\n",
              "      <td id=\"T_f79c4_row1_col2\" class=\"data row1 col2\" >negative</td>\n",
              "      <td id=\"T_f79c4_row1_col3\" class=\"data row1 col3\" >negative</td>\n",
              "      <td id=\"T_f79c4_row1_col4\" class=\"data row1 col4\" >-0.610300</td>\n",
              "      <td id=\"T_f79c4_row1_col5\" class=\"data row1 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row1_col6\" class=\"data row1 col6\" >0.997500</td>\n",
              "      <td id=\"T_f79c4_row1_col7\" class=\"data row1 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row1_col8\" class=\"data row1 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row2\" class=\"row_heading level0 row2\" >DPRVUI8Q</th>\n",
              "      <td id=\"T_f79c4_row2_col0\" class=\"data row2 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row2_col1\" class=\"data row2 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row2_col2\" class=\"data row2 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row2_col3\" class=\"data row2 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row2_col4\" class=\"data row2 col4\" >0.985600</td>\n",
              "      <td id=\"T_f79c4_row2_col5\" class=\"data row2 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row2_col6\" class=\"data row2 col6\" >0.997000</td>\n",
              "      <td id=\"T_f79c4_row2_col7\" class=\"data row2 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row2_col8\" class=\"data row2 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row3\" class=\"row_heading level0 row3\" >G2XQWE7E</th>\n",
              "      <td id=\"T_f79c4_row3_col0\" class=\"data row3 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row3_col1\" class=\"data row3 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row3_col2\" class=\"data row3 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row3_col3\" class=\"data row3 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row3_col4\" class=\"data row3 col4\" >0.984200</td>\n",
              "      <td id=\"T_f79c4_row3_col5\" class=\"data row3 col5\" >negative</td>\n",
              "      <td id=\"T_f79c4_row3_col6\" class=\"data row3 col6\" >-0.538700</td>\n",
              "      <td id=\"T_f79c4_row3_col7\" class=\"data row3 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row3_col8\" class=\"data row3 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row4\" class=\"row_heading level0 row4\" >LU2I7PCZ</th>\n",
              "      <td id=\"T_f79c4_row4_col0\" class=\"data row4 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row4_col1\" class=\"data row4 col1\" >negative</td>\n",
              "      <td id=\"T_f79c4_row4_col2\" class=\"data row4 col2\" >negative</td>\n",
              "      <td id=\"T_f79c4_row4_col3\" class=\"data row4 col3\" >negative</td>\n",
              "      <td id=\"T_f79c4_row4_col4\" class=\"data row4 col4\" >-0.916900</td>\n",
              "      <td id=\"T_f79c4_row4_col5\" class=\"data row4 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row4_col6\" class=\"data row4 col6\" >0.270900</td>\n",
              "      <td id=\"T_f79c4_row4_col7\" class=\"data row4 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row4_col8\" class=\"data row4 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row5\" class=\"row_heading level0 row5\" >DLNP7CTM</th>\n",
              "      <td id=\"T_f79c4_row5_col0\" class=\"data row5 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row5_col1\" class=\"data row5 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row5_col2\" class=\"data row5 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row5_col3\" class=\"data row5 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row5_col4\" class=\"data row5 col4\" >0.986500</td>\n",
              "      <td id=\"T_f79c4_row5_col5\" class=\"data row5 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row5_col6\" class=\"data row5 col6\" >0.899100</td>\n",
              "      <td id=\"T_f79c4_row5_col7\" class=\"data row5 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row5_col8\" class=\"data row5 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row6\" class=\"row_heading level0 row6\" >USR5PKR8</th>\n",
              "      <td id=\"T_f79c4_row6_col0\" class=\"data row6 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row6_col1\" class=\"data row6 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row6_col2\" class=\"data row6 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row6_col3\" class=\"data row6 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row6_col4\" class=\"data row6 col4\" >0.918600</td>\n",
              "      <td id=\"T_f79c4_row6_col5\" class=\"data row6 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row6_col6\" class=\"data row6 col6\" >0.941700</td>\n",
              "      <td id=\"T_f79c4_row6_col7\" class=\"data row6 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row6_col8\" class=\"data row6 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row7\" class=\"row_heading level0 row7\" >Q3QL32QP</th>\n",
              "      <td id=\"T_f79c4_row7_col0\" class=\"data row7 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row7_col1\" class=\"data row7 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row7_col2\" class=\"data row7 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row7_col3\" class=\"data row7 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row7_col4\" class=\"data row7 col4\" >0.981200</td>\n",
              "      <td id=\"T_f79c4_row7_col5\" class=\"data row7 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row7_col6\" class=\"data row7 col6\" >0.904300</td>\n",
              "      <td id=\"T_f79c4_row7_col7\" class=\"data row7 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row7_col8\" class=\"data row7 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row8\" class=\"row_heading level0 row8\" >BN3LGG2S</th>\n",
              "      <td id=\"T_f79c4_row8_col0\" class=\"data row8 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row8_col1\" class=\"data row8 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row8_col2\" class=\"data row8 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row8_col3\" class=\"data row8 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row8_col4\" class=\"data row8 col4\" >0.986100</td>\n",
              "      <td id=\"T_f79c4_row8_col5\" class=\"data row8 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row8_col6\" class=\"data row8 col6\" >0.986800</td>\n",
              "      <td id=\"T_f79c4_row8_col7\" class=\"data row8 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row8_col8\" class=\"data row8 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row9\" class=\"row_heading level0 row9\" >PJSEZGQ4</th>\n",
              "      <td id=\"T_f79c4_row9_col0\" class=\"data row9 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row9_col1\" class=\"data row9 col1\" >negative</td>\n",
              "      <td id=\"T_f79c4_row9_col2\" class=\"data row9 col2\" >negative</td>\n",
              "      <td id=\"T_f79c4_row9_col3\" class=\"data row9 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row9_col4\" class=\"data row9 col4\" >0.971900</td>\n",
              "      <td id=\"T_f79c4_row9_col5\" class=\"data row9 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row9_col6\" class=\"data row9 col6\" >0.452700</td>\n",
              "      <td id=\"T_f79c4_row9_col7\" class=\"data row9 col7\" >Falsely Positive</td>\n",
              "      <td id=\"T_f79c4_row9_col8\" class=\"data row9 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row10\" class=\"row_heading level0 row10\" >NYXXADML</th>\n",
              "      <td id=\"T_f79c4_row10_col0\" class=\"data row10 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row10_col1\" class=\"data row10 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row10_col2\" class=\"data row10 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row10_col3\" class=\"data row10 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row10_col4\" class=\"data row10 col4\" >0.941300</td>\n",
              "      <td id=\"T_f79c4_row10_col5\" class=\"data row10 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row10_col6\" class=\"data row10 col6\" >0.990600</td>\n",
              "      <td id=\"T_f79c4_row10_col7\" class=\"data row10 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row10_col8\" class=\"data row10 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row11\" class=\"row_heading level0 row11\" >8HRFLNP9</th>\n",
              "      <td id=\"T_f79c4_row11_col0\" class=\"data row11 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row11_col1\" class=\"data row11 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row11_col2\" class=\"data row11 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row11_col3\" class=\"data row11 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row11_col4\" class=\"data row11 col4\" >0.933700</td>\n",
              "      <td id=\"T_f79c4_row11_col5\" class=\"data row11 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row11_col6\" class=\"data row11 col6\" >0.526700</td>\n",
              "      <td id=\"T_f79c4_row11_col7\" class=\"data row11 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row11_col8\" class=\"data row11 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row12\" class=\"row_heading level0 row12\" >FKFS95J3</th>\n",
              "      <td id=\"T_f79c4_row12_col0\" class=\"data row12 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row12_col1\" class=\"data row12 col1\" >mixed</td>\n",
              "      <td id=\"T_f79c4_row12_col2\" class=\"data row12 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row12_col3\" class=\"data row12 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row12_col4\" class=\"data row12 col4\" >0.956500</td>\n",
              "      <td id=\"T_f79c4_row12_col5\" class=\"data row12 col5\" >negative</td>\n",
              "      <td id=\"T_f79c4_row12_col6\" class=\"data row12 col6\" >-0.963100</td>\n",
              "      <td id=\"T_f79c4_row12_col7\" class=\"data row12 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row12_col8\" class=\"data row12 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row13\" class=\"row_heading level0 row13\" >WYMPWWPX</th>\n",
              "      <td id=\"T_f79c4_row13_col0\" class=\"data row13 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row13_col1\" class=\"data row13 col1\" >negative</td>\n",
              "      <td id=\"T_f79c4_row13_col2\" class=\"data row13 col2\" >negative</td>\n",
              "      <td id=\"T_f79c4_row13_col3\" class=\"data row13 col3\" >negative</td>\n",
              "      <td id=\"T_f79c4_row13_col4\" class=\"data row13 col4\" >-0.694300</td>\n",
              "      <td id=\"T_f79c4_row13_col5\" class=\"data row13 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row13_col6\" class=\"data row13 col6\" >0.889200</td>\n",
              "      <td id=\"T_f79c4_row13_col7\" class=\"data row13 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row13_col8\" class=\"data row13 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row14\" class=\"row_heading level0 row14\" >RHDC7PGQ</th>\n",
              "      <td id=\"T_f79c4_row14_col0\" class=\"data row14 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row14_col1\" class=\"data row14 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row14_col2\" class=\"data row14 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row14_col3\" class=\"data row14 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row14_col4\" class=\"data row14 col4\" >0.973200</td>\n",
              "      <td id=\"T_f79c4_row14_col5\" class=\"data row14 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row14_col6\" class=\"data row14 col6\" >0.994400</td>\n",
              "      <td id=\"T_f79c4_row14_col7\" class=\"data row14 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row14_col8\" class=\"data row14 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row15\" class=\"row_heading level0 row15\" >7PQP3M9K</th>\n",
              "      <td id=\"T_f79c4_row15_col0\" class=\"data row15 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row15_col1\" class=\"data row15 col1\" >mixed</td>\n",
              "      <td id=\"T_f79c4_row15_col2\" class=\"data row15 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row15_col3\" class=\"data row15 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row15_col4\" class=\"data row15 col4\" >0.895700</td>\n",
              "      <td id=\"T_f79c4_row15_col5\" class=\"data row15 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row15_col6\" class=\"data row15 col6\" >0.894700</td>\n",
              "      <td id=\"T_f79c4_row15_col7\" class=\"data row15 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row15_col8\" class=\"data row15 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row16\" class=\"row_heading level0 row16\" >INTVKS32</th>\n",
              "      <td id=\"T_f79c4_row16_col0\" class=\"data row16 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row16_col1\" class=\"data row16 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row16_col2\" class=\"data row16 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row16_col3\" class=\"data row16 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row16_col4\" class=\"data row16 col4\" >0.917900</td>\n",
              "      <td id=\"T_f79c4_row16_col5\" class=\"data row16 col5\" >negative</td>\n",
              "      <td id=\"T_f79c4_row16_col6\" class=\"data row16 col6\" >-0.860600</td>\n",
              "      <td id=\"T_f79c4_row16_col7\" class=\"data row16 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row16_col8\" class=\"data row16 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row17\" class=\"row_heading level0 row17\" >ZTCJYPXU</th>\n",
              "      <td id=\"T_f79c4_row17_col0\" class=\"data row17 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row17_col1\" class=\"data row17 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row17_col2\" class=\"data row17 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row17_col3\" class=\"data row17 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row17_col4\" class=\"data row17 col4\" >0.854900</td>\n",
              "      <td id=\"T_f79c4_row17_col5\" class=\"data row17 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row17_col6\" class=\"data row17 col6\" >0.976100</td>\n",
              "      <td id=\"T_f79c4_row17_col7\" class=\"data row17 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row17_col8\" class=\"data row17 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row18\" class=\"row_heading level0 row18\" >FRSRUXTD</th>\n",
              "      <td id=\"T_f79c4_row18_col0\" class=\"data row18 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row18_col1\" class=\"data row18 col1\" >negative</td>\n",
              "      <td id=\"T_f79c4_row18_col2\" class=\"data row18 col2\" >negative</td>\n",
              "      <td id=\"T_f79c4_row18_col3\" class=\"data row18 col3\" >negative</td>\n",
              "      <td id=\"T_f79c4_row18_col4\" class=\"data row18 col4\" >-0.880900</td>\n",
              "      <td id=\"T_f79c4_row18_col5\" class=\"data row18 col5\" >negative</td>\n",
              "      <td id=\"T_f79c4_row18_col6\" class=\"data row18 col6\" >-0.896900</td>\n",
              "      <td id=\"T_f79c4_row18_col7\" class=\"data row18 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row18_col8\" class=\"data row18 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row19\" class=\"row_heading level0 row19\" >9GRRTHPF</th>\n",
              "      <td id=\"T_f79c4_row19_col0\" class=\"data row19 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row19_col1\" class=\"data row19 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row19_col2\" class=\"data row19 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row19_col3\" class=\"data row19 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row19_col4\" class=\"data row19 col4\" >0.958700</td>\n",
              "      <td id=\"T_f79c4_row19_col5\" class=\"data row19 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row19_col6\" class=\"data row19 col6\" >0.841300</td>\n",
              "      <td id=\"T_f79c4_row19_col7\" class=\"data row19 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row19_col8\" class=\"data row19 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row20\" class=\"row_heading level0 row20\" >IP5MVLIS</th>\n",
              "      <td id=\"T_f79c4_row20_col0\" class=\"data row20 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row20_col1\" class=\"data row20 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row20_col2\" class=\"data row20 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row20_col3\" class=\"data row20 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row20_col4\" class=\"data row20 col4\" >0.986500</td>\n",
              "      <td id=\"T_f79c4_row20_col5\" class=\"data row20 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row20_col6\" class=\"data row20 col6\" >0.987700</td>\n",
              "      <td id=\"T_f79c4_row20_col7\" class=\"data row20 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row20_col8\" class=\"data row20 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row21\" class=\"row_heading level0 row21\" >VP4N4RXD</th>\n",
              "      <td id=\"T_f79c4_row21_col0\" class=\"data row21 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row21_col1\" class=\"data row21 col1\" >mixed</td>\n",
              "      <td id=\"T_f79c4_row21_col2\" class=\"data row21 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row21_col3\" class=\"data row21 col3\" >mixed</td>\n",
              "      <td id=\"T_f79c4_row21_col4\" class=\"data row21 col4\" >-0.132800</td>\n",
              "      <td id=\"T_f79c4_row21_col5\" class=\"data row21 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row21_col6\" class=\"data row21 col6\" >0.937100</td>\n",
              "      <td id=\"T_f79c4_row21_col7\" class=\"data row21 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row21_col8\" class=\"data row21 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row22\" class=\"row_heading level0 row22\" >R7U9G8WW</th>\n",
              "      <td id=\"T_f79c4_row22_col0\" class=\"data row22 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row22_col1\" class=\"data row22 col1\" >mixed</td>\n",
              "      <td id=\"T_f79c4_row22_col2\" class=\"data row22 col2\" >negative</td>\n",
              "      <td id=\"T_f79c4_row22_col3\" class=\"data row22 col3\" >negative</td>\n",
              "      <td id=\"T_f79c4_row22_col4\" class=\"data row22 col4\" >-0.967400</td>\n",
              "      <td id=\"T_f79c4_row22_col5\" class=\"data row22 col5\" >negative</td>\n",
              "      <td id=\"T_f79c4_row22_col6\" class=\"data row22 col6\" >-0.989000</td>\n",
              "      <td id=\"T_f79c4_row22_col7\" class=\"data row22 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row22_col8\" class=\"data row22 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row23\" class=\"row_heading level0 row23\" >IVYGKGI6</th>\n",
              "      <td id=\"T_f79c4_row23_col0\" class=\"data row23 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row23_col1\" class=\"data row23 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row23_col2\" class=\"data row23 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row23_col3\" class=\"data row23 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row23_col4\" class=\"data row23 col4\" >0.346800</td>\n",
              "      <td id=\"T_f79c4_row23_col5\" class=\"data row23 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row23_col6\" class=\"data row23 col6\" >0.983800</td>\n",
              "      <td id=\"T_f79c4_row23_col7\" class=\"data row23 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row23_col8\" class=\"data row23 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row24\" class=\"row_heading level0 row24\" >IW33CA4K</th>\n",
              "      <td id=\"T_f79c4_row24_col0\" class=\"data row24 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row24_col1\" class=\"data row24 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row24_col2\" class=\"data row24 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row24_col3\" class=\"data row24 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row24_col4\" class=\"data row24 col4\" >0.970900</td>\n",
              "      <td id=\"T_f79c4_row24_col5\" class=\"data row24 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row24_col6\" class=\"data row24 col6\" >0.977600</td>\n",
              "      <td id=\"T_f79c4_row24_col7\" class=\"data row24 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row24_col8\" class=\"data row24 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row25\" class=\"row_heading level0 row25\" >6UIRPZMY</th>\n",
              "      <td id=\"T_f79c4_row25_col0\" class=\"data row25 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row25_col1\" class=\"data row25 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row25_col2\" class=\"data row25 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row25_col3\" class=\"data row25 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row25_col4\" class=\"data row25 col4\" >0.945900</td>\n",
              "      <td id=\"T_f79c4_row25_col5\" class=\"data row25 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row25_col6\" class=\"data row25 col6\" >0.935700</td>\n",
              "      <td id=\"T_f79c4_row25_col7\" class=\"data row25 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row25_col8\" class=\"data row25 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row26\" class=\"row_heading level0 row26\" >P4DEV4FN</th>\n",
              "      <td id=\"T_f79c4_row26_col0\" class=\"data row26 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row26_col1\" class=\"data row26 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row26_col2\" class=\"data row26 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row26_col3\" class=\"data row26 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row26_col4\" class=\"data row26 col4\" >0.952400</td>\n",
              "      <td id=\"T_f79c4_row26_col5\" class=\"data row26 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row26_col6\" class=\"data row26 col6\" >0.994600</td>\n",
              "      <td id=\"T_f79c4_row26_col7\" class=\"data row26 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row26_col8\" class=\"data row26 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row27\" class=\"row_heading level0 row27\" >ZEAWWQZS</th>\n",
              "      <td id=\"T_f79c4_row27_col0\" class=\"data row27 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row27_col1\" class=\"data row27 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row27_col2\" class=\"data row27 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row27_col3\" class=\"data row27 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row27_col4\" class=\"data row27 col4\" >0.726900</td>\n",
              "      <td id=\"T_f79c4_row27_col5\" class=\"data row27 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row27_col6\" class=\"data row27 col6\" >0.934400</td>\n",
              "      <td id=\"T_f79c4_row27_col7\" class=\"data row27 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row27_col8\" class=\"data row27 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row28\" class=\"row_heading level0 row28\" >IKAXVKBT</th>\n",
              "      <td id=\"T_f79c4_row28_col0\" class=\"data row28 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row28_col1\" class=\"data row28 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row28_col2\" class=\"data row28 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row28_col3\" class=\"data row28 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row28_col4\" class=\"data row28 col4\" >0.977600</td>\n",
              "      <td id=\"T_f79c4_row28_col5\" class=\"data row28 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row28_col6\" class=\"data row28 col6\" >0.883500</td>\n",
              "      <td id=\"T_f79c4_row28_col7\" class=\"data row28 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row28_col8\" class=\"data row28 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row29\" class=\"row_heading level0 row29\" >VNXHI9SG</th>\n",
              "      <td id=\"T_f79c4_row29_col0\" class=\"data row29 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row29_col1\" class=\"data row29 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row29_col2\" class=\"data row29 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row29_col3\" class=\"data row29 col3\" >negative</td>\n",
              "      <td id=\"T_f79c4_row29_col4\" class=\"data row29 col4\" >-0.361200</td>\n",
              "      <td id=\"T_f79c4_row29_col5\" class=\"data row29 col5\" >negative</td>\n",
              "      <td id=\"T_f79c4_row29_col6\" class=\"data row29 col6\" >-0.986100</td>\n",
              "      <td id=\"T_f79c4_row29_col7\" class=\"data row29 col7\" >Falsely Negative</td>\n",
              "      <td id=\"T_f79c4_row29_col8\" class=\"data row29 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row30\" class=\"row_heading level0 row30\" >JDMUZ7Z7</th>\n",
              "      <td id=\"T_f79c4_row30_col0\" class=\"data row30 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row30_col1\" class=\"data row30 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row30_col2\" class=\"data row30 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row30_col3\" class=\"data row30 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row30_col4\" class=\"data row30 col4\" >0.970500</td>\n",
              "      <td id=\"T_f79c4_row30_col5\" class=\"data row30 col5\" >negative</td>\n",
              "      <td id=\"T_f79c4_row30_col6\" class=\"data row30 col6\" >-0.579300</td>\n",
              "      <td id=\"T_f79c4_row30_col7\" class=\"data row30 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row30_col8\" class=\"data row30 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row31\" class=\"row_heading level0 row31\" >8GUXXTQF</th>\n",
              "      <td id=\"T_f79c4_row31_col0\" class=\"data row31 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row31_col1\" class=\"data row31 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row31_col2\" class=\"data row31 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row31_col3\" class=\"data row31 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row31_col4\" class=\"data row31 col4\" >0.802000</td>\n",
              "      <td id=\"T_f79c4_row31_col5\" class=\"data row31 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row31_col6\" class=\"data row31 col6\" >0.979500</td>\n",
              "      <td id=\"T_f79c4_row31_col7\" class=\"data row31 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row31_col8\" class=\"data row31 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row32\" class=\"row_heading level0 row32\" >JRW9WYAV</th>\n",
              "      <td id=\"T_f79c4_row32_col0\" class=\"data row32 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row32_col1\" class=\"data row32 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row32_col2\" class=\"data row32 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row32_col3\" class=\"data row32 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row32_col4\" class=\"data row32 col4\" >0.978300</td>\n",
              "      <td id=\"T_f79c4_row32_col5\" class=\"data row32 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row32_col6\" class=\"data row32 col6\" >0.991300</td>\n",
              "      <td id=\"T_f79c4_row32_col7\" class=\"data row32 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row32_col8\" class=\"data row32 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row33\" class=\"row_heading level0 row33\" >4VYDTF8V</th>\n",
              "      <td id=\"T_f79c4_row33_col0\" class=\"data row33 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row33_col1\" class=\"data row33 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row33_col2\" class=\"data row33 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row33_col3\" class=\"data row33 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row33_col4\" class=\"data row33 col4\" >0.994300</td>\n",
              "      <td id=\"T_f79c4_row33_col5\" class=\"data row33 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row33_col6\" class=\"data row33 col6\" >0.996900</td>\n",
              "      <td id=\"T_f79c4_row33_col7\" class=\"data row33 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row33_col8\" class=\"data row33 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row34\" class=\"row_heading level0 row34\" >KXD2FAMV</th>\n",
              "      <td id=\"T_f79c4_row34_col0\" class=\"data row34 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row34_col1\" class=\"data row34 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row34_col2\" class=\"data row34 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row34_col3\" class=\"data row34 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row34_col4\" class=\"data row34 col4\" >0.982800</td>\n",
              "      <td id=\"T_f79c4_row34_col5\" class=\"data row34 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row34_col6\" class=\"data row34 col6\" >0.997700</td>\n",
              "      <td id=\"T_f79c4_row34_col7\" class=\"data row34 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row34_col8\" class=\"data row34 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row35\" class=\"row_heading level0 row35\" >HJX37TGK</th>\n",
              "      <td id=\"T_f79c4_row35_col0\" class=\"data row35 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row35_col1\" class=\"data row35 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row35_col2\" class=\"data row35 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row35_col3\" class=\"data row35 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row35_col4\" class=\"data row35 col4\" >0.926300</td>\n",
              "      <td id=\"T_f79c4_row35_col5\" class=\"data row35 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row35_col6\" class=\"data row35 col6\" >0.993100</td>\n",
              "      <td id=\"T_f79c4_row35_col7\" class=\"data row35 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row35_col8\" class=\"data row35 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row36\" class=\"row_heading level0 row36\" >5BCPT84V</th>\n",
              "      <td id=\"T_f79c4_row36_col0\" class=\"data row36 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row36_col1\" class=\"data row36 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row36_col2\" class=\"data row36 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row36_col3\" class=\"data row36 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row36_col4\" class=\"data row36 col4\" >0.955200</td>\n",
              "      <td id=\"T_f79c4_row36_col5\" class=\"data row36 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row36_col6\" class=\"data row36 col6\" >0.995500</td>\n",
              "      <td id=\"T_f79c4_row36_col7\" class=\"data row36 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row36_col8\" class=\"data row36 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row37\" class=\"row_heading level0 row37\" >FWT8J86D</th>\n",
              "      <td id=\"T_f79c4_row37_col0\" class=\"data row37 col0\" >Caligari</td>\n",
              "      <td id=\"T_f79c4_row37_col1\" class=\"data row37 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row37_col2\" class=\"data row37 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row37_col3\" class=\"data row37 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row37_col4\" class=\"data row37 col4\" >0.535800</td>\n",
              "      <td id=\"T_f79c4_row37_col5\" class=\"data row37 col5\" >negative</td>\n",
              "      <td id=\"T_f79c4_row37_col6\" class=\"data row37 col6\" >-0.995500</td>\n",
              "      <td id=\"T_f79c4_row37_col7\" class=\"data row37 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row37_col8\" class=\"data row37 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row38\" class=\"row_heading level0 row38\" >KIUF2ZGM</th>\n",
              "      <td id=\"T_f79c4_row38_col0\" class=\"data row38 col0\" >Metropolis</td>\n",
              "      <td id=\"T_f79c4_row38_col1\" class=\"data row38 col1\" >mixed</td>\n",
              "      <td id=\"T_f79c4_row38_col2\" class=\"data row38 col2\" >negative</td>\n",
              "      <td id=\"T_f79c4_row38_col3\" class=\"data row38 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row38_col4\" class=\"data row38 col4\" >0.502300</td>\n",
              "      <td id=\"T_f79c4_row38_col5\" class=\"data row38 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row38_col6\" class=\"data row38 col6\" >0.981100</td>\n",
              "      <td id=\"T_f79c4_row38_col7\" class=\"data row38 col7\" >Falsely Positive</td>\n",
              "      <td id=\"T_f79c4_row38_col8\" class=\"data row38 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row39\" class=\"row_heading level0 row39\" >NNLYWZ9X</th>\n",
              "      <td id=\"T_f79c4_row39_col0\" class=\"data row39 col0\" >Metropolis</td>\n",
              "      <td id=\"T_f79c4_row39_col1\" class=\"data row39 col1\" >mixed</td>\n",
              "      <td id=\"T_f79c4_row39_col2\" class=\"data row39 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row39_col3\" class=\"data row39 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row39_col4\" class=\"data row39 col4\" >0.947600</td>\n",
              "      <td id=\"T_f79c4_row39_col5\" class=\"data row39 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row39_col6\" class=\"data row39 col6\" >0.990200</td>\n",
              "      <td id=\"T_f79c4_row39_col7\" class=\"data row39 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row39_col8\" class=\"data row39 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row40\" class=\"row_heading level0 row40\" >EIRLBFNR</th>\n",
              "      <td id=\"T_f79c4_row40_col0\" class=\"data row40 col0\" >Metropolis</td>\n",
              "      <td id=\"T_f79c4_row40_col1\" class=\"data row40 col1\" >mixed</td>\n",
              "      <td id=\"T_f79c4_row40_col2\" class=\"data row40 col2\" >negative</td>\n",
              "      <td id=\"T_f79c4_row40_col3\" class=\"data row40 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row40_col4\" class=\"data row40 col4\" >0.519200</td>\n",
              "      <td id=\"T_f79c4_row40_col5\" class=\"data row40 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row40_col6\" class=\"data row40 col6\" >0.993800</td>\n",
              "      <td id=\"T_f79c4_row40_col7\" class=\"data row40 col7\" >Falsely Positive</td>\n",
              "      <td id=\"T_f79c4_row40_col8\" class=\"data row40 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row41\" class=\"row_heading level0 row41\" >JUZY74XR</th>\n",
              "      <td id=\"T_f79c4_row41_col0\" class=\"data row41 col0\" >Metropolis</td>\n",
              "      <td id=\"T_f79c4_row41_col1\" class=\"data row41 col1\" >mixed</td>\n",
              "      <td id=\"T_f79c4_row41_col2\" class=\"data row41 col2\" >negative</td>\n",
              "      <td id=\"T_f79c4_row41_col3\" class=\"data row41 col3\" >negative</td>\n",
              "      <td id=\"T_f79c4_row41_col4\" class=\"data row41 col4\" >-0.745400</td>\n",
              "      <td id=\"T_f79c4_row41_col5\" class=\"data row41 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row41_col6\" class=\"data row41 col6\" >0.987700</td>\n",
              "      <td id=\"T_f79c4_row41_col7\" class=\"data row41 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row41_col8\" class=\"data row41 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row42\" class=\"row_heading level0 row42\" >XPTBGFBL</th>\n",
              "      <td id=\"T_f79c4_row42_col0\" class=\"data row42 col0\" >Metropolis</td>\n",
              "      <td id=\"T_f79c4_row42_col1\" class=\"data row42 col1\" >mixed</td>\n",
              "      <td id=\"T_f79c4_row42_col2\" class=\"data row42 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row42_col3\" class=\"data row42 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row42_col4\" class=\"data row42 col4\" >0.981100</td>\n",
              "      <td id=\"T_f79c4_row42_col5\" class=\"data row42 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row42_col6\" class=\"data row42 col6\" >0.996700</td>\n",
              "      <td id=\"T_f79c4_row42_col7\" class=\"data row42 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row42_col8\" class=\"data row42 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row43\" class=\"row_heading level0 row43\" >KCDMVU3Q</th>\n",
              "      <td id=\"T_f79c4_row43_col0\" class=\"data row43 col0\" >Metropolis</td>\n",
              "      <td id=\"T_f79c4_row43_col1\" class=\"data row43 col1\" >negative</td>\n",
              "      <td id=\"T_f79c4_row43_col2\" class=\"data row43 col2\" >negative</td>\n",
              "      <td id=\"T_f79c4_row43_col3\" class=\"data row43 col3\" >negative</td>\n",
              "      <td id=\"T_f79c4_row43_col4\" class=\"data row43 col4\" >-0.955200</td>\n",
              "      <td id=\"T_f79c4_row43_col5\" class=\"data row43 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row43_col6\" class=\"data row43 col6\" >0.996000</td>\n",
              "      <td id=\"T_f79c4_row43_col7\" class=\"data row43 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row43_col8\" class=\"data row43 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row44\" class=\"row_heading level0 row44\" >H2W3WCDI</th>\n",
              "      <td id=\"T_f79c4_row44_col0\" class=\"data row44 col0\" >Metropolis</td>\n",
              "      <td id=\"T_f79c4_row44_col1\" class=\"data row44 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row44_col2\" class=\"data row44 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row44_col3\" class=\"data row44 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row44_col4\" class=\"data row44 col4\" >0.987900</td>\n",
              "      <td id=\"T_f79c4_row44_col5\" class=\"data row44 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row44_col6\" class=\"data row44 col6\" >0.998200</td>\n",
              "      <td id=\"T_f79c4_row44_col7\" class=\"data row44 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row44_col8\" class=\"data row44 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row45\" class=\"row_heading level0 row45\" >R4Z87JRJ</th>\n",
              "      <td id=\"T_f79c4_row45_col0\" class=\"data row45 col0\" >Metropolis</td>\n",
              "      <td id=\"T_f79c4_row45_col1\" class=\"data row45 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row45_col2\" class=\"data row45 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row45_col3\" class=\"data row45 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row45_col4\" class=\"data row45 col4\" >0.981800</td>\n",
              "      <td id=\"T_f79c4_row45_col5\" class=\"data row45 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row45_col6\" class=\"data row45 col6\" >0.996200</td>\n",
              "      <td id=\"T_f79c4_row45_col7\" class=\"data row45 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row45_col8\" class=\"data row45 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row46\" class=\"row_heading level0 row46\" >TIRHYW52</th>\n",
              "      <td id=\"T_f79c4_row46_col0\" class=\"data row46 col0\" >Metropolis</td>\n",
              "      <td id=\"T_f79c4_row46_col1\" class=\"data row46 col1\" >mixed</td>\n",
              "      <td id=\"T_f79c4_row46_col2\" class=\"data row46 col2\" >negative</td>\n",
              "      <td id=\"T_f79c4_row46_col3\" class=\"data row46 col3\" >mixed</td>\n",
              "      <td id=\"T_f79c4_row46_col4\" class=\"data row46 col4\" >-0.063400</td>\n",
              "      <td id=\"T_f79c4_row46_col5\" class=\"data row46 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row46_col6\" class=\"data row46 col6\" >0.927400</td>\n",
              "      <td id=\"T_f79c4_row46_col7\" class=\"data row46 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row46_col8\" class=\"data row46 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row47\" class=\"row_heading level0 row47\" >TAKRH7XK</th>\n",
              "      <td id=\"T_f79c4_row47_col0\" class=\"data row47 col0\" >Metropolis</td>\n",
              "      <td id=\"T_f79c4_row47_col1\" class=\"data row47 col1\" >negative</td>\n",
              "      <td id=\"T_f79c4_row47_col2\" class=\"data row47 col2\" >negative</td>\n",
              "      <td id=\"T_f79c4_row47_col3\" class=\"data row47 col3\" >negative</td>\n",
              "      <td id=\"T_f79c4_row47_col4\" class=\"data row47 col4\" >-0.948600</td>\n",
              "      <td id=\"T_f79c4_row47_col5\" class=\"data row47 col5\" >negative</td>\n",
              "      <td id=\"T_f79c4_row47_col6\" class=\"data row47 col6\" >-0.398200</td>\n",
              "      <td id=\"T_f79c4_row47_col7\" class=\"data row47 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row47_col8\" class=\"data row47 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row48\" class=\"row_heading level0 row48\" >RQXPTUFX</th>\n",
              "      <td id=\"T_f79c4_row48_col0\" class=\"data row48 col0\" >Metropolis</td>\n",
              "      <td id=\"T_f79c4_row48_col1\" class=\"data row48 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row48_col2\" class=\"data row48 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row48_col3\" class=\"data row48 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row48_col4\" class=\"data row48 col4\" >0.908100</td>\n",
              "      <td id=\"T_f79c4_row48_col5\" class=\"data row48 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row48_col6\" class=\"data row48 col6\" >0.838900</td>\n",
              "      <td id=\"T_f79c4_row48_col7\" class=\"data row48 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row48_col8\" class=\"data row48 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row49\" class=\"row_heading level0 row49\" >8IKY2MZ9</th>\n",
              "      <td id=\"T_f79c4_row49_col0\" class=\"data row49 col0\" >Metropolis</td>\n",
              "      <td id=\"T_f79c4_row49_col1\" class=\"data row49 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row49_col2\" class=\"data row49 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row49_col3\" class=\"data row49 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row49_col4\" class=\"data row49 col4\" >0.984200</td>\n",
              "      <td id=\"T_f79c4_row49_col5\" class=\"data row49 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row49_col6\" class=\"data row49 col6\" >0.999200</td>\n",
              "      <td id=\"T_f79c4_row49_col7\" class=\"data row49 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row49_col8\" class=\"data row49 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row50\" class=\"row_heading level0 row50\" >AXSMZ7XF</th>\n",
              "      <td id=\"T_f79c4_row50_col0\" class=\"data row50 col0\" >Metropolis</td>\n",
              "      <td id=\"T_f79c4_row50_col1\" class=\"data row50 col1\" >mixed</td>\n",
              "      <td id=\"T_f79c4_row50_col2\" class=\"data row50 col2\" >negative</td>\n",
              "      <td id=\"T_f79c4_row50_col3\" class=\"data row50 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row50_col4\" class=\"data row50 col4\" >0.518700</td>\n",
              "      <td id=\"T_f79c4_row50_col5\" class=\"data row50 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row50_col6\" class=\"data row50 col6\" >0.992400</td>\n",
              "      <td id=\"T_f79c4_row50_col7\" class=\"data row50 col7\" >Falsely Positive</td>\n",
              "      <td id=\"T_f79c4_row50_col8\" class=\"data row50 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row51\" class=\"row_heading level0 row51\" >HPQLWRGP</th>\n",
              "      <td id=\"T_f79c4_row51_col0\" class=\"data row51 col0\" >Metropolis</td>\n",
              "      <td id=\"T_f79c4_row51_col1\" class=\"data row51 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row51_col2\" class=\"data row51 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row51_col3\" class=\"data row51 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row51_col4\" class=\"data row51 col4\" >0.979600</td>\n",
              "      <td id=\"T_f79c4_row51_col5\" class=\"data row51 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row51_col6\" class=\"data row51 col6\" >0.999500</td>\n",
              "      <td id=\"T_f79c4_row51_col7\" class=\"data row51 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row51_col8\" class=\"data row51 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row52\" class=\"row_heading level0 row52\" >C5HFH5UD</th>\n",
              "      <td id=\"T_f79c4_row52_col0\" class=\"data row52 col0\" >Metropolis</td>\n",
              "      <td id=\"T_f79c4_row52_col1\" class=\"data row52 col1\" >negative</td>\n",
              "      <td id=\"T_f79c4_row52_col2\" class=\"data row52 col2\" >negative</td>\n",
              "      <td id=\"T_f79c4_row52_col3\" class=\"data row52 col3\" >negative</td>\n",
              "      <td id=\"T_f79c4_row52_col4\" class=\"data row52 col4\" >-0.829600</td>\n",
              "      <td id=\"T_f79c4_row52_col5\" class=\"data row52 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row52_col6\" class=\"data row52 col6\" >0.902700</td>\n",
              "      <td id=\"T_f79c4_row52_col7\" class=\"data row52 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row52_col8\" class=\"data row52 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row53\" class=\"row_heading level0 row53\" >5KUVWUIP</th>\n",
              "      <td id=\"T_f79c4_row53_col0\" class=\"data row53 col0\" >Metropolis</td>\n",
              "      <td id=\"T_f79c4_row53_col1\" class=\"data row53 col1\" >negative</td>\n",
              "      <td id=\"T_f79c4_row53_col2\" class=\"data row53 col2\" >negative</td>\n",
              "      <td id=\"T_f79c4_row53_col3\" class=\"data row53 col3\" >negative</td>\n",
              "      <td id=\"T_f79c4_row53_col4\" class=\"data row53 col4\" >-0.920100</td>\n",
              "      <td id=\"T_f79c4_row53_col5\" class=\"data row53 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row53_col6\" class=\"data row53 col6\" >0.995100</td>\n",
              "      <td id=\"T_f79c4_row53_col7\" class=\"data row53 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row53_col8\" class=\"data row53 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row54\" class=\"row_heading level0 row54\" >EPFXMBDW</th>\n",
              "      <td id=\"T_f79c4_row54_col0\" class=\"data row54 col0\" >Metropolis</td>\n",
              "      <td id=\"T_f79c4_row54_col1\" class=\"data row54 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row54_col2\" class=\"data row54 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row54_col3\" class=\"data row54 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row54_col4\" class=\"data row54 col4\" >0.945500</td>\n",
              "      <td id=\"T_f79c4_row54_col5\" class=\"data row54 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row54_col6\" class=\"data row54 col6\" >0.970500</td>\n",
              "      <td id=\"T_f79c4_row54_col7\" class=\"data row54 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row54_col8\" class=\"data row54 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row55\" class=\"row_heading level0 row55\" >7WEGPP4X</th>\n",
              "      <td id=\"T_f79c4_row55_col0\" class=\"data row55 col0\" >Metropolis</td>\n",
              "      <td id=\"T_f79c4_row55_col1\" class=\"data row55 col1\" >mixed</td>\n",
              "      <td id=\"T_f79c4_row55_col2\" class=\"data row55 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row55_col3\" class=\"data row55 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row55_col4\" class=\"data row55 col4\" >0.888500</td>\n",
              "      <td id=\"T_f79c4_row55_col5\" class=\"data row55 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row55_col6\" class=\"data row55 col6\" >0.587100</td>\n",
              "      <td id=\"T_f79c4_row55_col7\" class=\"data row55 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row55_col8\" class=\"data row55 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row56\" class=\"row_heading level0 row56\" >ZV2KYKDS</th>\n",
              "      <td id=\"T_f79c4_row56_col0\" class=\"data row56 col0\" >Metropolis</td>\n",
              "      <td id=\"T_f79c4_row56_col1\" class=\"data row56 col1\" >mixed</td>\n",
              "      <td id=\"T_f79c4_row56_col2\" class=\"data row56 col2\" >negative</td>\n",
              "      <td id=\"T_f79c4_row56_col3\" class=\"data row56 col3\" >negative</td>\n",
              "      <td id=\"T_f79c4_row56_col4\" class=\"data row56 col4\" >-0.901300</td>\n",
              "      <td id=\"T_f79c4_row56_col5\" class=\"data row56 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row56_col6\" class=\"data row56 col6\" >0.997700</td>\n",
              "      <td id=\"T_f79c4_row56_col7\" class=\"data row56 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row56_col8\" class=\"data row56 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row57\" class=\"row_heading level0 row57\" >D4AR9KYS</th>\n",
              "      <td id=\"T_f79c4_row57_col0\" class=\"data row57 col0\" >Metropolis</td>\n",
              "      <td id=\"T_f79c4_row57_col1\" class=\"data row57 col1\" >negative</td>\n",
              "      <td id=\"T_f79c4_row57_col2\" class=\"data row57 col2\" >negative</td>\n",
              "      <td id=\"T_f79c4_row57_col3\" class=\"data row57 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row57_col4\" class=\"data row57 col4\" >0.981000</td>\n",
              "      <td id=\"T_f79c4_row57_col5\" class=\"data row57 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row57_col6\" class=\"data row57 col6\" >0.997800</td>\n",
              "      <td id=\"T_f79c4_row57_col7\" class=\"data row57 col7\" >Falsely Positive</td>\n",
              "      <td id=\"T_f79c4_row57_col8\" class=\"data row57 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row58\" class=\"row_heading level0 row58\" >AFLARXAU</th>\n",
              "      <td id=\"T_f79c4_row58_col0\" class=\"data row58 col0\" >Metropolis</td>\n",
              "      <td id=\"T_f79c4_row58_col1\" class=\"data row58 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row58_col2\" class=\"data row58 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row58_col3\" class=\"data row58 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row58_col4\" class=\"data row58 col4\" >0.921700</td>\n",
              "      <td id=\"T_f79c4_row58_col5\" class=\"data row58 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row58_col6\" class=\"data row58 col6\" >0.969100</td>\n",
              "      <td id=\"T_f79c4_row58_col7\" class=\"data row58 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row58_col8\" class=\"data row58 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row59\" class=\"row_heading level0 row59\" >3XNICQ2K</th>\n",
              "      <td id=\"T_f79c4_row59_col0\" class=\"data row59 col0\" >Metropolis</td>\n",
              "      <td id=\"T_f79c4_row59_col1\" class=\"data row59 col1\" >mixed</td>\n",
              "      <td id=\"T_f79c4_row59_col2\" class=\"data row59 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row59_col3\" class=\"data row59 col3\" >mixed</td>\n",
              "      <td id=\"T_f79c4_row59_col4\" class=\"data row59 col4\" >-0.066100</td>\n",
              "      <td id=\"T_f79c4_row59_col5\" class=\"data row59 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row59_col6\" class=\"data row59 col6\" >0.952400</td>\n",
              "      <td id=\"T_f79c4_row59_col7\" class=\"data row59 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row59_col8\" class=\"data row59 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row60\" class=\"row_heading level0 row60\" >RM78NSKJ</th>\n",
              "      <td id=\"T_f79c4_row60_col0\" class=\"data row60 col0\" >Metropolis</td>\n",
              "      <td id=\"T_f79c4_row60_col1\" class=\"data row60 col1\" >mixed</td>\n",
              "      <td id=\"T_f79c4_row60_col2\" class=\"data row60 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row60_col3\" class=\"data row60 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row60_col4\" class=\"data row60 col4\" >0.974000</td>\n",
              "      <td id=\"T_f79c4_row60_col5\" class=\"data row60 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row60_col6\" class=\"data row60 col6\" >0.996600</td>\n",
              "      <td id=\"T_f79c4_row60_col7\" class=\"data row60 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row60_col8\" class=\"data row60 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row61\" class=\"row_heading level0 row61\" >CZWZ5UV7</th>\n",
              "      <td id=\"T_f79c4_row61_col0\" class=\"data row61 col0\" >Metropolis</td>\n",
              "      <td id=\"T_f79c4_row61_col1\" class=\"data row61 col1\" >mixed</td>\n",
              "      <td id=\"T_f79c4_row61_col2\" class=\"data row61 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row61_col3\" class=\"data row61 col3\" >negative</td>\n",
              "      <td id=\"T_f79c4_row61_col4\" class=\"data row61 col4\" >-0.700300</td>\n",
              "      <td id=\"T_f79c4_row61_col5\" class=\"data row61 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row61_col6\" class=\"data row61 col6\" >0.996100</td>\n",
              "      <td id=\"T_f79c4_row61_col7\" class=\"data row61 col7\" >Falsely Negative</td>\n",
              "      <td id=\"T_f79c4_row61_col8\" class=\"data row61 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row62\" class=\"row_heading level0 row62\" >TM6H4KLS</th>\n",
              "      <td id=\"T_f79c4_row62_col0\" class=\"data row62 col0\" >Metropolis</td>\n",
              "      <td id=\"T_f79c4_row62_col1\" class=\"data row62 col1\" >mixed</td>\n",
              "      <td id=\"T_f79c4_row62_col2\" class=\"data row62 col2\" >negative</td>\n",
              "      <td id=\"T_f79c4_row62_col3\" class=\"data row62 col3\" >negative</td>\n",
              "      <td id=\"T_f79c4_row62_col4\" class=\"data row62 col4\" >-0.868100</td>\n",
              "      <td id=\"T_f79c4_row62_col5\" class=\"data row62 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row62_col6\" class=\"data row62 col6\" >0.998000</td>\n",
              "      <td id=\"T_f79c4_row62_col7\" class=\"data row62 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row62_col8\" class=\"data row62 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row63\" class=\"row_heading level0 row63\" >8WNFN9AE</th>\n",
              "      <td id=\"T_f79c4_row63_col0\" class=\"data row63 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_f79c4_row63_col1\" class=\"data row63 col1\" >negative</td>\n",
              "      <td id=\"T_f79c4_row63_col2\" class=\"data row63 col2\" >negative</td>\n",
              "      <td id=\"T_f79c4_row63_col3\" class=\"data row63 col3\" >negative</td>\n",
              "      <td id=\"T_f79c4_row63_col4\" class=\"data row63 col4\" >-0.971300</td>\n",
              "      <td id=\"T_f79c4_row63_col5\" class=\"data row63 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row63_col6\" class=\"data row63 col6\" >0.869600</td>\n",
              "      <td id=\"T_f79c4_row63_col7\" class=\"data row63 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row63_col8\" class=\"data row63 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row64\" class=\"row_heading level0 row64\" >5YA546KU</th>\n",
              "      <td id=\"T_f79c4_row64_col0\" class=\"data row64 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_f79c4_row64_col1\" class=\"data row64 col1\" >mixed</td>\n",
              "      <td id=\"T_f79c4_row64_col2\" class=\"data row64 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row64_col3\" class=\"data row64 col3\" >negative</td>\n",
              "      <td id=\"T_f79c4_row64_col4\" class=\"data row64 col4\" >-0.880700</td>\n",
              "      <td id=\"T_f79c4_row64_col5\" class=\"data row64 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row64_col6\" class=\"data row64 col6\" >0.666600</td>\n",
              "      <td id=\"T_f79c4_row64_col7\" class=\"data row64 col7\" >Falsely Negative</td>\n",
              "      <td id=\"T_f79c4_row64_col8\" class=\"data row64 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row65\" class=\"row_heading level0 row65\" >N88X5HAE</th>\n",
              "      <td id=\"T_f79c4_row65_col0\" class=\"data row65 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_f79c4_row65_col1\" class=\"data row65 col1\" >negative</td>\n",
              "      <td id=\"T_f79c4_row65_col2\" class=\"data row65 col2\" >negative</td>\n",
              "      <td id=\"T_f79c4_row65_col3\" class=\"data row65 col3\" >negative</td>\n",
              "      <td id=\"T_f79c4_row65_col4\" class=\"data row65 col4\" >-0.721200</td>\n",
              "      <td id=\"T_f79c4_row65_col5\" class=\"data row65 col5\" >negative</td>\n",
              "      <td id=\"T_f79c4_row65_col6\" class=\"data row65 col6\" >-0.690200</td>\n",
              "      <td id=\"T_f79c4_row65_col7\" class=\"data row65 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row65_col8\" class=\"data row65 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row66\" class=\"row_heading level0 row66\" >9W4Z3TFU</th>\n",
              "      <td id=\"T_f79c4_row66_col0\" class=\"data row66 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_f79c4_row66_col1\" class=\"data row66 col1\" >mixed</td>\n",
              "      <td id=\"T_f79c4_row66_col2\" class=\"data row66 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row66_col3\" class=\"data row66 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row66_col4\" class=\"data row66 col4\" >0.928700</td>\n",
              "      <td id=\"T_f79c4_row66_col5\" class=\"data row66 col5\" >negative</td>\n",
              "      <td id=\"T_f79c4_row66_col6\" class=\"data row66 col6\" >-0.995600</td>\n",
              "      <td id=\"T_f79c4_row66_col7\" class=\"data row66 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row66_col8\" class=\"data row66 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row67\" class=\"row_heading level0 row67\" >K7EEHSC6</th>\n",
              "      <td id=\"T_f79c4_row67_col0\" class=\"data row67 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_f79c4_row67_col1\" class=\"data row67 col1\" >mixed</td>\n",
              "      <td id=\"T_f79c4_row67_col2\" class=\"data row67 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row67_col3\" class=\"data row67 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row67_col4\" class=\"data row67 col4\" >0.931300</td>\n",
              "      <td id=\"T_f79c4_row67_col5\" class=\"data row67 col5\" >negative</td>\n",
              "      <td id=\"T_f79c4_row67_col6\" class=\"data row67 col6\" >-0.891600</td>\n",
              "      <td id=\"T_f79c4_row67_col7\" class=\"data row67 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row67_col8\" class=\"data row67 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row68\" class=\"row_heading level0 row68\" >783K9P3V</th>\n",
              "      <td id=\"T_f79c4_row68_col0\" class=\"data row68 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_f79c4_row68_col1\" class=\"data row68 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row68_col2\" class=\"data row68 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row68_col3\" class=\"data row68 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row68_col4\" class=\"data row68 col4\" >0.796800</td>\n",
              "      <td id=\"T_f79c4_row68_col5\" class=\"data row68 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row68_col6\" class=\"data row68 col6\" >0.986100</td>\n",
              "      <td id=\"T_f79c4_row68_col7\" class=\"data row68 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row68_col8\" class=\"data row68 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row69\" class=\"row_heading level0 row69\" >B39A5NLE</th>\n",
              "      <td id=\"T_f79c4_row69_col0\" class=\"data row69 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_f79c4_row69_col1\" class=\"data row69 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row69_col2\" class=\"data row69 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row69_col3\" class=\"data row69 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row69_col4\" class=\"data row69 col4\" >0.973500</td>\n",
              "      <td id=\"T_f79c4_row69_col5\" class=\"data row69 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row69_col6\" class=\"data row69 col6\" >0.968700</td>\n",
              "      <td id=\"T_f79c4_row69_col7\" class=\"data row69 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row69_col8\" class=\"data row69 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row70\" class=\"row_heading level0 row70\" >9VL8I3DT</th>\n",
              "      <td id=\"T_f79c4_row70_col0\" class=\"data row70 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_f79c4_row70_col1\" class=\"data row70 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row70_col2\" class=\"data row70 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row70_col3\" class=\"data row70 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row70_col4\" class=\"data row70 col4\" >0.987100</td>\n",
              "      <td id=\"T_f79c4_row70_col5\" class=\"data row70 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row70_col6\" class=\"data row70 col6\" >0.855300</td>\n",
              "      <td id=\"T_f79c4_row70_col7\" class=\"data row70 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row70_col8\" class=\"data row70 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row71\" class=\"row_heading level0 row71\" >J8GLYGC5</th>\n",
              "      <td id=\"T_f79c4_row71_col0\" class=\"data row71 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_f79c4_row71_col1\" class=\"data row71 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row71_col2\" class=\"data row71 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row71_col3\" class=\"data row71 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row71_col4\" class=\"data row71 col4\" >0.964800</td>\n",
              "      <td id=\"T_f79c4_row71_col5\" class=\"data row71 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row71_col6\" class=\"data row71 col6\" >0.998400</td>\n",
              "      <td id=\"T_f79c4_row71_col7\" class=\"data row71 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row71_col8\" class=\"data row71 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row72\" class=\"row_heading level0 row72\" >4HBY3K9J</th>\n",
              "      <td id=\"T_f79c4_row72_col0\" class=\"data row72 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_f79c4_row72_col1\" class=\"data row72 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row72_col2\" class=\"data row72 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row72_col3\" class=\"data row72 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row72_col4\" class=\"data row72 col4\" >0.916900</td>\n",
              "      <td id=\"T_f79c4_row72_col5\" class=\"data row72 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row72_col6\" class=\"data row72 col6\" >0.980300</td>\n",
              "      <td id=\"T_f79c4_row72_col7\" class=\"data row72 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row72_col8\" class=\"data row72 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row73\" class=\"row_heading level0 row73\" >YEUQS36A</th>\n",
              "      <td id=\"T_f79c4_row73_col0\" class=\"data row73 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_f79c4_row73_col1\" class=\"data row73 col1\" >mixed</td>\n",
              "      <td id=\"T_f79c4_row73_col2\" class=\"data row73 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row73_col3\" class=\"data row73 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row73_col4\" class=\"data row73 col4\" >0.931300</td>\n",
              "      <td id=\"T_f79c4_row73_col5\" class=\"data row73 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row73_col6\" class=\"data row73 col6\" >0.992500</td>\n",
              "      <td id=\"T_f79c4_row73_col7\" class=\"data row73 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row73_col8\" class=\"data row73 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row74\" class=\"row_heading level0 row74\" >CEHEIWR7</th>\n",
              "      <td id=\"T_f79c4_row74_col0\" class=\"data row74 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_f79c4_row74_col1\" class=\"data row74 col1\" >mixed</td>\n",
              "      <td id=\"T_f79c4_row74_col2\" class=\"data row74 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row74_col3\" class=\"data row74 col3\" >negative</td>\n",
              "      <td id=\"T_f79c4_row74_col4\" class=\"data row74 col4\" >-0.911800</td>\n",
              "      <td id=\"T_f79c4_row74_col5\" class=\"data row74 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row74_col6\" class=\"data row74 col6\" >0.578900</td>\n",
              "      <td id=\"T_f79c4_row74_col7\" class=\"data row74 col7\" >Falsely Negative</td>\n",
              "      <td id=\"T_f79c4_row74_col8\" class=\"data row74 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row75\" class=\"row_heading level0 row75\" >2SQ2YDPA</th>\n",
              "      <td id=\"T_f79c4_row75_col0\" class=\"data row75 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_f79c4_row75_col1\" class=\"data row75 col1\" >mixed</td>\n",
              "      <td id=\"T_f79c4_row75_col2\" class=\"data row75 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row75_col3\" class=\"data row75 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row75_col4\" class=\"data row75 col4\" >0.739400</td>\n",
              "      <td id=\"T_f79c4_row75_col5\" class=\"data row75 col5\" >negative</td>\n",
              "      <td id=\"T_f79c4_row75_col6\" class=\"data row75 col6\" >-0.980200</td>\n",
              "      <td id=\"T_f79c4_row75_col7\" class=\"data row75 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row75_col8\" class=\"data row75 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row76\" class=\"row_heading level0 row76\" >KWC9KUTP</th>\n",
              "      <td id=\"T_f79c4_row76_col0\" class=\"data row76 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_f79c4_row76_col1\" class=\"data row76 col1\" >negative</td>\n",
              "      <td id=\"T_f79c4_row76_col2\" class=\"data row76 col2\" >negative</td>\n",
              "      <td id=\"T_f79c4_row76_col3\" class=\"data row76 col3\" >negative</td>\n",
              "      <td id=\"T_f79c4_row76_col4\" class=\"data row76 col4\" >-0.755500</td>\n",
              "      <td id=\"T_f79c4_row76_col5\" class=\"data row76 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row76_col6\" class=\"data row76 col6\" >0.994100</td>\n",
              "      <td id=\"T_f79c4_row76_col7\" class=\"data row76 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row76_col8\" class=\"data row76 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row77\" class=\"row_heading level0 row77\" >LDB96ADR</th>\n",
              "      <td id=\"T_f79c4_row77_col0\" class=\"data row77 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_f79c4_row77_col1\" class=\"data row77 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row77_col2\" class=\"data row77 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row77_col3\" class=\"data row77 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row77_col4\" class=\"data row77 col4\" >0.945100</td>\n",
              "      <td id=\"T_f79c4_row77_col5\" class=\"data row77 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row77_col6\" class=\"data row77 col6\" >0.987200</td>\n",
              "      <td id=\"T_f79c4_row77_col7\" class=\"data row77 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row77_col8\" class=\"data row77 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row78\" class=\"row_heading level0 row78\" >SBML59AF</th>\n",
              "      <td id=\"T_f79c4_row78_col0\" class=\"data row78 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_f79c4_row78_col1\" class=\"data row78 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row78_col2\" class=\"data row78 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row78_col3\" class=\"data row78 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row78_col4\" class=\"data row78 col4\" >0.916900</td>\n",
              "      <td id=\"T_f79c4_row78_col5\" class=\"data row78 col5\" >positive</td>\n",
              "      <td id=\"T_f79c4_row78_col6\" class=\"data row78 col6\" >0.340000</td>\n",
              "      <td id=\"T_f79c4_row78_col7\" class=\"data row78 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row78_col8\" class=\"data row78 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f79c4_level0_row79\" class=\"row_heading level0 row79\" >L7JBS7LN</th>\n",
              "      <td id=\"T_f79c4_row79_col0\" class=\"data row79 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_f79c4_row79_col1\" class=\"data row79 col1\" >positive</td>\n",
              "      <td id=\"T_f79c4_row79_col2\" class=\"data row79 col2\" >positive</td>\n",
              "      <td id=\"T_f79c4_row79_col3\" class=\"data row79 col3\" >positive</td>\n",
              "      <td id=\"T_f79c4_row79_col4\" class=\"data row79 col4\" >0.943300</td>\n",
              "      <td id=\"T_f79c4_row79_col5\" class=\"data row79 col5\" >negative</td>\n",
              "      <td id=\"T_f79c4_row79_col6\" class=\"data row79 col6\" >-0.969200</td>\n",
              "      <td id=\"T_f79c4_row79_col7\" class=\"data row79 col7\" >Correct</td>\n",
              "      <td id=\"T_f79c4_row79_col8\" class=\"data row79 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Let's get a subset of this table with only the reviews where there is some disagreement between the human and machine sentiment analyses\n"
      ],
      "metadata": {
        "id": "h1AhF5_8UAv9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "divergent = sentiment[(sentiment['GPT_Error_Check'] != \"Correct\") | (sentiment['VADER_Error_Check'] != \"Correct\")]"
      ],
      "metadata": {
        "id": "pFGtmIyZWqh_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply the custom function to style \"divergent\"\n",
        "divergent_map = divergent.style.applymap(map_sentiment_to_color, subset=['Manual_Judgment', 'Binary_Judgment', 'VADER_review_sentiment', 'VADER_ChatGPT_sentiment'])\\\n",
        "    .set_properties(**{\n",
        "        'text-align': 'center',\n",
        "        'font-size': '16px'  # You can adjust the font size as desired\n",
        "    })\\\n",
        "    .background_gradient(cmap=cmapR, subset=['VADER_review_score', 'VADER_ChatGPT_score'], vmin=-1, vmax=1)\\\n",
        "    .applymap(map_errors_to_color, subset=['GPT_Error_Check', 'VADER_Error_Check'])\n",
        "\n",
        "# Save the styled DataFrame to an HTML file\n",
        "divergent_map.to_html('divergent_map.html')"
      ],
      "metadata": {
        "id": "sbKXQrxGwaDH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "divergent_map"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "wJM4bokDXJrf",
        "outputId": "7aace399-a9dd-4391-b800-62de467e0a9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7c7c71718c40>"
            ],
            "text/html": [
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              "#T_c025c_row0_col0, #T_c025c_row1_col0, #T_c025c_row2_col0, #T_c025c_row3_col0, #T_c025c_row4_col0, #T_c025c_row5_col0, #T_c025c_row6_col0, #T_c025c_row7_col0, #T_c025c_row8_col0, #T_c025c_row9_col0, #T_c025c_row10_col0, #T_c025c_row11_col0, #T_c025c_row12_col0, #T_c025c_row13_col0, #T_c025c_row14_col0, #T_c025c_row15_col0, #T_c025c_row16_col0, #T_c025c_row17_col0, #T_c025c_row18_col0, #T_c025c_row19_col0, #T_c025c_row20_col0, #T_c025c_row21_col0, #T_c025c_row22_col0, #T_c025c_row23_col0, #T_c025c_row24_col0, #T_c025c_row25_col0, #T_c025c_row26_col0, #T_c025c_row27_col0, #T_c025c_row28_col0, #T_c025c_row29_col0 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "}\n",
              "#T_c025c_row0_col1, #T_c025c_row0_col2, #T_c025c_row0_col3, #T_c025c_row1_col5, #T_c025c_row2_col1, #T_c025c_row2_col2, #T_c025c_row2_col3, #T_c025c_row3_col1, #T_c025c_row3_col2, #T_c025c_row4_col5, #T_c025c_row5_col1, #T_c025c_row5_col2, #T_c025c_row5_col3, #T_c025c_row6_col5, #T_c025c_row7_col3, #T_c025c_row7_col5, #T_c025c_row8_col5, #T_c025c_row9_col5, #T_c025c_row10_col2, #T_c025c_row11_col2, #T_c025c_row12_col2, #T_c025c_row12_col3, #T_c025c_row13_col1, #T_c025c_row13_col2, #T_c025c_row13_col3, #T_c025c_row14_col2, #T_c025c_row15_col2, #T_c025c_row16_col1, #T_c025c_row16_col2, #T_c025c_row16_col3, #T_c025c_row17_col1, #T_c025c_row17_col2, #T_c025c_row17_col3, #T_c025c_row18_col2, #T_c025c_row18_col3, #T_c025c_row19_col1, #T_c025c_row19_col2, #T_c025c_row20_col3, #T_c025c_row21_col2, #T_c025c_row21_col3, #T_c025c_row22_col1, #T_c025c_row22_col2, #T_c025c_row22_col3, #T_c025c_row23_col3, #T_c025c_row24_col5, #T_c025c_row25_col5, #T_c025c_row26_col3, #T_c025c_row27_col5, #T_c025c_row28_col1, #T_c025c_row28_col2, #T_c025c_row28_col3, #T_c025c_row29_col5 {\n",
              "  background-color: firebrick;\n",
              "  color: white;\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "}\n",
              "#T_c025c_row0_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #f26841;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row0_col5, #T_c025c_row1_col1, #T_c025c_row1_col2, #T_c025c_row1_col3, #T_c025c_row2_col5, #T_c025c_row3_col3, #T_c025c_row3_col5, #T_c025c_row4_col2, #T_c025c_row4_col3, #T_c025c_row5_col5, #T_c025c_row6_col1, #T_c025c_row6_col2, #T_c025c_row6_col3, #T_c025c_row7_col1, #T_c025c_row7_col2, #T_c025c_row8_col1, #T_c025c_row8_col2, #T_c025c_row8_col3, #T_c025c_row9_col1, #T_c025c_row9_col2, #T_c025c_row9_col3, #T_c025c_row10_col3, #T_c025c_row10_col5, #T_c025c_row11_col3, #T_c025c_row11_col5, #T_c025c_row12_col5, #T_c025c_row13_col5, #T_c025c_row14_col5, #T_c025c_row15_col3, #T_c025c_row15_col5, #T_c025c_row16_col5, #T_c025c_row17_col5, #T_c025c_row18_col5, #T_c025c_row19_col3, #T_c025c_row19_col5, #T_c025c_row20_col2, #T_c025c_row20_col5, #T_c025c_row21_col5, #T_c025c_row22_col5, #T_c025c_row23_col2, #T_c025c_row23_col5, #T_c025c_row24_col2, #T_c025c_row24_col3, #T_c025c_row25_col2, #T_c025c_row25_col3, #T_c025c_row26_col2, #T_c025c_row26_col5, #T_c025c_row27_col2, #T_c025c_row27_col3, #T_c025c_row28_col5, #T_c025c_row29_col1, #T_c025c_row29_col2, #T_c025c_row29_col3 {\n",
              "  background-color: darkgreen;\n",
              "  color: white;\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "}\n",
              "#T_c025c_row0_col6, #T_c025c_row11_col6, #T_c025c_row13_col6, #T_c025c_row15_col6, #T_c025c_row17_col6, #T_c025c_row18_col6, #T_c025c_row19_col6, #T_c025c_row20_col6, #T_c025c_row21_col6, #T_c025c_row28_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #006837;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row0_col7, #T_c025c_row1_col7, #T_c025c_row2_col7, #T_c025c_row4_col7, #T_c025c_row5_col7, #T_c025c_row6_col7, #T_c025c_row8_col7, #T_c025c_row9_col7, #T_c025c_row12_col7, #T_c025c_row13_col7, #T_c025c_row14_col7, #T_c025c_row16_col7, #T_c025c_row17_col7, #T_c025c_row18_col7, #T_c025c_row20_col8, #T_c025c_row21_col7, #T_c025c_row22_col7, #T_c025c_row23_col8, #T_c025c_row24_col7, #T_c025c_row25_col7, #T_c025c_row26_col8, #T_c025c_row27_col7, #T_c025c_row28_col7, #T_c025c_row29_col7 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: darkgreen;\n",
              "  color: white;\n",
              "}\n",
              "#T_c025c_row0_col8, #T_c025c_row2_col8, #T_c025c_row3_col7, #T_c025c_row3_col8, #T_c025c_row5_col8, #T_c025c_row10_col7, #T_c025c_row10_col8, #T_c025c_row11_col7, #T_c025c_row11_col8, #T_c025c_row12_col8, #T_c025c_row13_col8, #T_c025c_row14_col8, #T_c025c_row15_col7, #T_c025c_row15_col8, #T_c025c_row16_col8, #T_c025c_row17_col8, #T_c025c_row18_col8, #T_c025c_row19_col7, #T_c025c_row19_col8, #T_c025c_row21_col8, #T_c025c_row22_col8, #T_c025c_row28_col8 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: white;\n",
              "  color: darkgreen;\n",
              "}\n",
              "#T_c025c_row1_col4, #T_c025c_row10_col6, #T_c025c_row19_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #026c39;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row1_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #f7814c;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row1_col8, #T_c025c_row4_col8, #T_c025c_row6_col8, #T_c025c_row7_col7, #T_c025c_row7_col8, #T_c025c_row8_col8, #T_c025c_row9_col8, #T_c025c_row20_col7, #T_c025c_row23_col7, #T_c025c_row24_col8, #T_c025c_row25_col8, #T_c025c_row26_col7, #T_c025c_row27_col8, #T_c025c_row29_col8 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: white;\n",
              "  color: firebrick;\n",
              "}\n",
              "#T_c025c_row2_col4, #T_c025c_row17_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #b91326;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row2_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #c7e77f;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_c025c_row3_col4, #T_c025c_row8_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #036e3a;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row3_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #96d268;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_c025c_row4_col1, #T_c025c_row10_col1, #T_c025c_row11_col1, #T_c025c_row12_col1, #T_c025c_row14_col1, #T_c025c_row14_col3, #T_c025c_row15_col1, #T_c025c_row18_col1, #T_c025c_row20_col1, #T_c025c_row21_col1, #T_c025c_row23_col1, #T_c025c_row24_col1, #T_c025c_row25_col1, #T_c025c_row26_col1, #T_c025c_row27_col1 {\n",
              "  background-color: gold;\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "}\n",
              "#T_c025c_row4_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #05713c;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row4_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #ad0826;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row5_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #e65036;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row5_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #0e8245;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row6_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #0a7b41;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row6_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #c62027;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row7_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #fdb768;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_c025c_row7_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #a70226;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row8_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #f57245;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row9_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #7ac665;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_c025c_row9_col6, #T_c025c_row24_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #a50026;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row10_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #84ca66;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_c025c_row11_col4, #T_c025c_row15_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #7fc866;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_c025c_row12_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #de402e;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row12_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #016a38;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row13_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #af0926;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row14_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #fff5ae;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_c025c_row14_col6, #T_c025c_row24_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #097940;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row16_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #ce2827;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row16_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #0c7f43;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row18_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #bd1726;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row20_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #e54e35;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row21_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #c41e27;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row22_col4, #T_c025c_row29_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #ab0626;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row22_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #108647;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row23_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #c21c27;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row23_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #4bb05c;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row25_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #08773f;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row25_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #be1827;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row26_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #bb1526;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row26_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #6bbf64;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_c025c_row27_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #30a356;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row27_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #a90426;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row28_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #dd3d2d;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c025c_row29_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #07753e;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_c025c\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_c025c_level0_col0\" class=\"col_heading level0 col0\" >Movie</th>\n",
              "      <th id=\"T_c025c_level0_col1\" class=\"col_heading level0 col1\" >Manual_Judgment</th>\n",
              "      <th id=\"T_c025c_level0_col2\" class=\"col_heading level0 col2\" >Binary_Judgment</th>\n",
              "      <th id=\"T_c025c_level0_col3\" class=\"col_heading level0 col3\" >VADER_ChatGPT_sentiment</th>\n",
              "      <th id=\"T_c025c_level0_col4\" class=\"col_heading level0 col4\" >VADER_ChatGPT_score</th>\n",
              "      <th id=\"T_c025c_level0_col5\" class=\"col_heading level0 col5\" >VADER_review_sentiment</th>\n",
              "      <th id=\"T_c025c_level0_col6\" class=\"col_heading level0 col6\" >VADER_review_score</th>\n",
              "      <th id=\"T_c025c_level0_col7\" class=\"col_heading level0 col7\" >GPT_Error_Check</th>\n",
              "      <th id=\"T_c025c_level0_col8\" class=\"col_heading level0 col8\" >VADER_Error_Check</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >ID</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "      <th class=\"blank col6\" >&nbsp;</th>\n",
              "      <th class=\"blank col7\" >&nbsp;</th>\n",
              "      <th class=\"blank col8\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row0\" class=\"row_heading level0 row0\" >MXZKVQ6B</th>\n",
              "      <td id=\"T_c025c_row0_col0\" class=\"data row0 col0\" >Caligari</td>\n",
              "      <td id=\"T_c025c_row0_col1\" class=\"data row0 col1\" >negative</td>\n",
              "      <td id=\"T_c025c_row0_col2\" class=\"data row0 col2\" >negative</td>\n",
              "      <td id=\"T_c025c_row0_col3\" class=\"data row0 col3\" >negative</td>\n",
              "      <td id=\"T_c025c_row0_col4\" class=\"data row0 col4\" >-0.610300</td>\n",
              "      <td id=\"T_c025c_row0_col5\" class=\"data row0 col5\" >positive</td>\n",
              "      <td id=\"T_c025c_row0_col6\" class=\"data row0 col6\" >0.997500</td>\n",
              "      <td id=\"T_c025c_row0_col7\" class=\"data row0 col7\" >Correct</td>\n",
              "      <td id=\"T_c025c_row0_col8\" class=\"data row0 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row1\" class=\"row_heading level0 row1\" >G2XQWE7E</th>\n",
              "      <td id=\"T_c025c_row1_col0\" class=\"data row1 col0\" >Caligari</td>\n",
              "      <td id=\"T_c025c_row1_col1\" class=\"data row1 col1\" >positive</td>\n",
              "      <td id=\"T_c025c_row1_col2\" class=\"data row1 col2\" >positive</td>\n",
              "      <td id=\"T_c025c_row1_col3\" class=\"data row1 col3\" >positive</td>\n",
              "      <td id=\"T_c025c_row1_col4\" class=\"data row1 col4\" >0.984200</td>\n",
              "      <td id=\"T_c025c_row1_col5\" class=\"data row1 col5\" >negative</td>\n",
              "      <td id=\"T_c025c_row1_col6\" class=\"data row1 col6\" >-0.538700</td>\n",
              "      <td id=\"T_c025c_row1_col7\" class=\"data row1 col7\" >Correct</td>\n",
              "      <td id=\"T_c025c_row1_col8\" class=\"data row1 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row2\" class=\"row_heading level0 row2\" >LU2I7PCZ</th>\n",
              "      <td id=\"T_c025c_row2_col0\" class=\"data row2 col0\" >Caligari</td>\n",
              "      <td id=\"T_c025c_row2_col1\" class=\"data row2 col1\" >negative</td>\n",
              "      <td id=\"T_c025c_row2_col2\" class=\"data row2 col2\" >negative</td>\n",
              "      <td id=\"T_c025c_row2_col3\" class=\"data row2 col3\" >negative</td>\n",
              "      <td id=\"T_c025c_row2_col4\" class=\"data row2 col4\" >-0.916900</td>\n",
              "      <td id=\"T_c025c_row2_col5\" class=\"data row2 col5\" >positive</td>\n",
              "      <td id=\"T_c025c_row2_col6\" class=\"data row2 col6\" >0.270900</td>\n",
              "      <td id=\"T_c025c_row2_col7\" class=\"data row2 col7\" >Correct</td>\n",
              "      <td id=\"T_c025c_row2_col8\" class=\"data row2 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row3\" class=\"row_heading level0 row3\" >PJSEZGQ4</th>\n",
              "      <td id=\"T_c025c_row3_col0\" class=\"data row3 col0\" >Caligari</td>\n",
              "      <td id=\"T_c025c_row3_col1\" class=\"data row3 col1\" >negative</td>\n",
              "      <td id=\"T_c025c_row3_col2\" class=\"data row3 col2\" >negative</td>\n",
              "      <td id=\"T_c025c_row3_col3\" class=\"data row3 col3\" >positive</td>\n",
              "      <td id=\"T_c025c_row3_col4\" class=\"data row3 col4\" >0.971900</td>\n",
              "      <td id=\"T_c025c_row3_col5\" class=\"data row3 col5\" >positive</td>\n",
              "      <td id=\"T_c025c_row3_col6\" class=\"data row3 col6\" >0.452700</td>\n",
              "      <td id=\"T_c025c_row3_col7\" class=\"data row3 col7\" >Falsely Positive</td>\n",
              "      <td id=\"T_c025c_row3_col8\" class=\"data row3 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row4\" class=\"row_heading level0 row4\" >FKFS95J3</th>\n",
              "      <td id=\"T_c025c_row4_col0\" class=\"data row4 col0\" >Caligari</td>\n",
              "      <td id=\"T_c025c_row4_col1\" class=\"data row4 col1\" >mixed</td>\n",
              "      <td id=\"T_c025c_row4_col2\" class=\"data row4 col2\" >positive</td>\n",
              "      <td id=\"T_c025c_row4_col3\" class=\"data row4 col3\" >positive</td>\n",
              "      <td id=\"T_c025c_row4_col4\" class=\"data row4 col4\" >0.956500</td>\n",
              "      <td id=\"T_c025c_row4_col5\" class=\"data row4 col5\" >negative</td>\n",
              "      <td id=\"T_c025c_row4_col6\" class=\"data row4 col6\" >-0.963100</td>\n",
              "      <td id=\"T_c025c_row4_col7\" class=\"data row4 col7\" >Correct</td>\n",
              "      <td id=\"T_c025c_row4_col8\" class=\"data row4 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row5\" class=\"row_heading level0 row5\" >WYMPWWPX</th>\n",
              "      <td id=\"T_c025c_row5_col0\" class=\"data row5 col0\" >Caligari</td>\n",
              "      <td id=\"T_c025c_row5_col1\" class=\"data row5 col1\" >negative</td>\n",
              "      <td id=\"T_c025c_row5_col2\" class=\"data row5 col2\" >negative</td>\n",
              "      <td id=\"T_c025c_row5_col3\" class=\"data row5 col3\" >negative</td>\n",
              "      <td id=\"T_c025c_row5_col4\" class=\"data row5 col4\" >-0.694300</td>\n",
              "      <td id=\"T_c025c_row5_col5\" class=\"data row5 col5\" >positive</td>\n",
              "      <td id=\"T_c025c_row5_col6\" class=\"data row5 col6\" >0.889200</td>\n",
              "      <td id=\"T_c025c_row5_col7\" class=\"data row5 col7\" >Correct</td>\n",
              "      <td id=\"T_c025c_row5_col8\" class=\"data row5 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row6\" class=\"row_heading level0 row6\" >INTVKS32</th>\n",
              "      <td id=\"T_c025c_row6_col0\" class=\"data row6 col0\" >Caligari</td>\n",
              "      <td id=\"T_c025c_row6_col1\" class=\"data row6 col1\" >positive</td>\n",
              "      <td id=\"T_c025c_row6_col2\" class=\"data row6 col2\" >positive</td>\n",
              "      <td id=\"T_c025c_row6_col3\" class=\"data row6 col3\" >positive</td>\n",
              "      <td id=\"T_c025c_row6_col4\" class=\"data row6 col4\" >0.917900</td>\n",
              "      <td id=\"T_c025c_row6_col5\" class=\"data row6 col5\" >negative</td>\n",
              "      <td id=\"T_c025c_row6_col6\" class=\"data row6 col6\" >-0.860600</td>\n",
              "      <td id=\"T_c025c_row6_col7\" class=\"data row6 col7\" >Correct</td>\n",
              "      <td id=\"T_c025c_row6_col8\" class=\"data row6 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row7\" class=\"row_heading level0 row7\" >VNXHI9SG</th>\n",
              "      <td id=\"T_c025c_row7_col0\" class=\"data row7 col0\" >Caligari</td>\n",
              "      <td id=\"T_c025c_row7_col1\" class=\"data row7 col1\" >positive</td>\n",
              "      <td id=\"T_c025c_row7_col2\" class=\"data row7 col2\" >positive</td>\n",
              "      <td id=\"T_c025c_row7_col3\" class=\"data row7 col3\" >negative</td>\n",
              "      <td id=\"T_c025c_row7_col4\" class=\"data row7 col4\" >-0.361200</td>\n",
              "      <td id=\"T_c025c_row7_col5\" class=\"data row7 col5\" >negative</td>\n",
              "      <td id=\"T_c025c_row7_col6\" class=\"data row7 col6\" >-0.986100</td>\n",
              "      <td id=\"T_c025c_row7_col7\" class=\"data row7 col7\" >Falsely Negative</td>\n",
              "      <td id=\"T_c025c_row7_col8\" class=\"data row7 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row8\" class=\"row_heading level0 row8\" >JDMUZ7Z7</th>\n",
              "      <td id=\"T_c025c_row8_col0\" class=\"data row8 col0\" >Caligari</td>\n",
              "      <td id=\"T_c025c_row8_col1\" class=\"data row8 col1\" >positive</td>\n",
              "      <td id=\"T_c025c_row8_col2\" class=\"data row8 col2\" >positive</td>\n",
              "      <td id=\"T_c025c_row8_col3\" class=\"data row8 col3\" >positive</td>\n",
              "      <td id=\"T_c025c_row8_col4\" class=\"data row8 col4\" >0.970500</td>\n",
              "      <td id=\"T_c025c_row8_col5\" class=\"data row8 col5\" >negative</td>\n",
              "      <td id=\"T_c025c_row8_col6\" class=\"data row8 col6\" >-0.579300</td>\n",
              "      <td id=\"T_c025c_row8_col7\" class=\"data row8 col7\" >Correct</td>\n",
              "      <td id=\"T_c025c_row8_col8\" class=\"data row8 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row9\" class=\"row_heading level0 row9\" >FWT8J86D</th>\n",
              "      <td id=\"T_c025c_row9_col0\" class=\"data row9 col0\" >Caligari</td>\n",
              "      <td id=\"T_c025c_row9_col1\" class=\"data row9 col1\" >positive</td>\n",
              "      <td id=\"T_c025c_row9_col2\" class=\"data row9 col2\" >positive</td>\n",
              "      <td id=\"T_c025c_row9_col3\" class=\"data row9 col3\" >positive</td>\n",
              "      <td id=\"T_c025c_row9_col4\" class=\"data row9 col4\" >0.535800</td>\n",
              "      <td id=\"T_c025c_row9_col5\" class=\"data row9 col5\" >negative</td>\n",
              "      <td id=\"T_c025c_row9_col6\" class=\"data row9 col6\" >-0.995500</td>\n",
              "      <td id=\"T_c025c_row9_col7\" class=\"data row9 col7\" >Correct</td>\n",
              "      <td id=\"T_c025c_row9_col8\" class=\"data row9 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row10\" class=\"row_heading level0 row10\" >KIUF2ZGM</th>\n",
              "      <td id=\"T_c025c_row10_col0\" class=\"data row10 col0\" >Metropolis</td>\n",
              "      <td id=\"T_c025c_row10_col1\" class=\"data row10 col1\" >mixed</td>\n",
              "      <td id=\"T_c025c_row10_col2\" class=\"data row10 col2\" >negative</td>\n",
              "      <td id=\"T_c025c_row10_col3\" class=\"data row10 col3\" >positive</td>\n",
              "      <td id=\"T_c025c_row10_col4\" class=\"data row10 col4\" >0.502300</td>\n",
              "      <td id=\"T_c025c_row10_col5\" class=\"data row10 col5\" >positive</td>\n",
              "      <td id=\"T_c025c_row10_col6\" class=\"data row10 col6\" >0.981100</td>\n",
              "      <td id=\"T_c025c_row10_col7\" class=\"data row10 col7\" >Falsely Positive</td>\n",
              "      <td id=\"T_c025c_row10_col8\" class=\"data row10 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row11\" class=\"row_heading level0 row11\" >EIRLBFNR</th>\n",
              "      <td id=\"T_c025c_row11_col0\" class=\"data row11 col0\" >Metropolis</td>\n",
              "      <td id=\"T_c025c_row11_col1\" class=\"data row11 col1\" >mixed</td>\n",
              "      <td id=\"T_c025c_row11_col2\" class=\"data row11 col2\" >negative</td>\n",
              "      <td id=\"T_c025c_row11_col3\" class=\"data row11 col3\" >positive</td>\n",
              "      <td id=\"T_c025c_row11_col4\" class=\"data row11 col4\" >0.519200</td>\n",
              "      <td id=\"T_c025c_row11_col5\" class=\"data row11 col5\" >positive</td>\n",
              "      <td id=\"T_c025c_row11_col6\" class=\"data row11 col6\" >0.993800</td>\n",
              "      <td id=\"T_c025c_row11_col7\" class=\"data row11 col7\" >Falsely Positive</td>\n",
              "      <td id=\"T_c025c_row11_col8\" class=\"data row11 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row12\" class=\"row_heading level0 row12\" >JUZY74XR</th>\n",
              "      <td id=\"T_c025c_row12_col0\" class=\"data row12 col0\" >Metropolis</td>\n",
              "      <td id=\"T_c025c_row12_col1\" class=\"data row12 col1\" >mixed</td>\n",
              "      <td id=\"T_c025c_row12_col2\" class=\"data row12 col2\" >negative</td>\n",
              "      <td id=\"T_c025c_row12_col3\" class=\"data row12 col3\" >negative</td>\n",
              "      <td id=\"T_c025c_row12_col4\" class=\"data row12 col4\" >-0.745400</td>\n",
              "      <td id=\"T_c025c_row12_col5\" class=\"data row12 col5\" >positive</td>\n",
              "      <td id=\"T_c025c_row12_col6\" class=\"data row12 col6\" >0.987700</td>\n",
              "      <td id=\"T_c025c_row12_col7\" class=\"data row12 col7\" >Correct</td>\n",
              "      <td id=\"T_c025c_row12_col8\" class=\"data row12 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row13\" class=\"row_heading level0 row13\" >KCDMVU3Q</th>\n",
              "      <td id=\"T_c025c_row13_col0\" class=\"data row13 col0\" >Metropolis</td>\n",
              "      <td id=\"T_c025c_row13_col1\" class=\"data row13 col1\" >negative</td>\n",
              "      <td id=\"T_c025c_row13_col2\" class=\"data row13 col2\" >negative</td>\n",
              "      <td id=\"T_c025c_row13_col3\" class=\"data row13 col3\" >negative</td>\n",
              "      <td id=\"T_c025c_row13_col4\" class=\"data row13 col4\" >-0.955200</td>\n",
              "      <td id=\"T_c025c_row13_col5\" class=\"data row13 col5\" >positive</td>\n",
              "      <td id=\"T_c025c_row13_col6\" class=\"data row13 col6\" >0.996000</td>\n",
              "      <td id=\"T_c025c_row13_col7\" class=\"data row13 col7\" >Correct</td>\n",
              "      <td id=\"T_c025c_row13_col8\" class=\"data row13 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row14\" class=\"row_heading level0 row14\" >TIRHYW52</th>\n",
              "      <td id=\"T_c025c_row14_col0\" class=\"data row14 col0\" >Metropolis</td>\n",
              "      <td id=\"T_c025c_row14_col1\" class=\"data row14 col1\" >mixed</td>\n",
              "      <td id=\"T_c025c_row14_col2\" class=\"data row14 col2\" >negative</td>\n",
              "      <td id=\"T_c025c_row14_col3\" class=\"data row14 col3\" >mixed</td>\n",
              "      <td id=\"T_c025c_row14_col4\" class=\"data row14 col4\" >-0.063400</td>\n",
              "      <td id=\"T_c025c_row14_col5\" class=\"data row14 col5\" >positive</td>\n",
              "      <td id=\"T_c025c_row14_col6\" class=\"data row14 col6\" >0.927400</td>\n",
              "      <td id=\"T_c025c_row14_col7\" class=\"data row14 col7\" >Correct</td>\n",
              "      <td id=\"T_c025c_row14_col8\" class=\"data row14 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row15\" class=\"row_heading level0 row15\" >AXSMZ7XF</th>\n",
              "      <td id=\"T_c025c_row15_col0\" class=\"data row15 col0\" >Metropolis</td>\n",
              "      <td id=\"T_c025c_row15_col1\" class=\"data row15 col1\" >mixed</td>\n",
              "      <td id=\"T_c025c_row15_col2\" class=\"data row15 col2\" >negative</td>\n",
              "      <td id=\"T_c025c_row15_col3\" class=\"data row15 col3\" >positive</td>\n",
              "      <td id=\"T_c025c_row15_col4\" class=\"data row15 col4\" >0.518700</td>\n",
              "      <td id=\"T_c025c_row15_col5\" class=\"data row15 col5\" >positive</td>\n",
              "      <td id=\"T_c025c_row15_col6\" class=\"data row15 col6\" >0.992400</td>\n",
              "      <td id=\"T_c025c_row15_col7\" class=\"data row15 col7\" >Falsely Positive</td>\n",
              "      <td id=\"T_c025c_row15_col8\" class=\"data row15 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row16\" class=\"row_heading level0 row16\" >C5HFH5UD</th>\n",
              "      <td id=\"T_c025c_row16_col0\" class=\"data row16 col0\" >Metropolis</td>\n",
              "      <td id=\"T_c025c_row16_col1\" class=\"data row16 col1\" >negative</td>\n",
              "      <td id=\"T_c025c_row16_col2\" class=\"data row16 col2\" >negative</td>\n",
              "      <td id=\"T_c025c_row16_col3\" class=\"data row16 col3\" >negative</td>\n",
              "      <td id=\"T_c025c_row16_col4\" class=\"data row16 col4\" >-0.829600</td>\n",
              "      <td id=\"T_c025c_row16_col5\" class=\"data row16 col5\" >positive</td>\n",
              "      <td id=\"T_c025c_row16_col6\" class=\"data row16 col6\" >0.902700</td>\n",
              "      <td id=\"T_c025c_row16_col7\" class=\"data row16 col7\" >Correct</td>\n",
              "      <td id=\"T_c025c_row16_col8\" class=\"data row16 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row17\" class=\"row_heading level0 row17\" >5KUVWUIP</th>\n",
              "      <td id=\"T_c025c_row17_col0\" class=\"data row17 col0\" >Metropolis</td>\n",
              "      <td id=\"T_c025c_row17_col1\" class=\"data row17 col1\" >negative</td>\n",
              "      <td id=\"T_c025c_row17_col2\" class=\"data row17 col2\" >negative</td>\n",
              "      <td id=\"T_c025c_row17_col3\" class=\"data row17 col3\" >negative</td>\n",
              "      <td id=\"T_c025c_row17_col4\" class=\"data row17 col4\" >-0.920100</td>\n",
              "      <td id=\"T_c025c_row17_col5\" class=\"data row17 col5\" >positive</td>\n",
              "      <td id=\"T_c025c_row17_col6\" class=\"data row17 col6\" >0.995100</td>\n",
              "      <td id=\"T_c025c_row17_col7\" class=\"data row17 col7\" >Correct</td>\n",
              "      <td id=\"T_c025c_row17_col8\" class=\"data row17 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row18\" class=\"row_heading level0 row18\" >ZV2KYKDS</th>\n",
              "      <td id=\"T_c025c_row18_col0\" class=\"data row18 col0\" >Metropolis</td>\n",
              "      <td id=\"T_c025c_row18_col1\" class=\"data row18 col1\" >mixed</td>\n",
              "      <td id=\"T_c025c_row18_col2\" class=\"data row18 col2\" >negative</td>\n",
              "      <td id=\"T_c025c_row18_col3\" class=\"data row18 col3\" >negative</td>\n",
              "      <td id=\"T_c025c_row18_col4\" class=\"data row18 col4\" >-0.901300</td>\n",
              "      <td id=\"T_c025c_row18_col5\" class=\"data row18 col5\" >positive</td>\n",
              "      <td id=\"T_c025c_row18_col6\" class=\"data row18 col6\" >0.997700</td>\n",
              "      <td id=\"T_c025c_row18_col7\" class=\"data row18 col7\" >Correct</td>\n",
              "      <td id=\"T_c025c_row18_col8\" class=\"data row18 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row19\" class=\"row_heading level0 row19\" >D4AR9KYS</th>\n",
              "      <td id=\"T_c025c_row19_col0\" class=\"data row19 col0\" >Metropolis</td>\n",
              "      <td id=\"T_c025c_row19_col1\" class=\"data row19 col1\" >negative</td>\n",
              "      <td id=\"T_c025c_row19_col2\" class=\"data row19 col2\" >negative</td>\n",
              "      <td id=\"T_c025c_row19_col3\" class=\"data row19 col3\" >positive</td>\n",
              "      <td id=\"T_c025c_row19_col4\" class=\"data row19 col4\" >0.981000</td>\n",
              "      <td id=\"T_c025c_row19_col5\" class=\"data row19 col5\" >positive</td>\n",
              "      <td id=\"T_c025c_row19_col6\" class=\"data row19 col6\" >0.997800</td>\n",
              "      <td id=\"T_c025c_row19_col7\" class=\"data row19 col7\" >Falsely Positive</td>\n",
              "      <td id=\"T_c025c_row19_col8\" class=\"data row19 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row20\" class=\"row_heading level0 row20\" >CZWZ5UV7</th>\n",
              "      <td id=\"T_c025c_row20_col0\" class=\"data row20 col0\" >Metropolis</td>\n",
              "      <td id=\"T_c025c_row20_col1\" class=\"data row20 col1\" >mixed</td>\n",
              "      <td id=\"T_c025c_row20_col2\" class=\"data row20 col2\" >positive</td>\n",
              "      <td id=\"T_c025c_row20_col3\" class=\"data row20 col3\" >negative</td>\n",
              "      <td id=\"T_c025c_row20_col4\" class=\"data row20 col4\" >-0.700300</td>\n",
              "      <td id=\"T_c025c_row20_col5\" class=\"data row20 col5\" >positive</td>\n",
              "      <td id=\"T_c025c_row20_col6\" class=\"data row20 col6\" >0.996100</td>\n",
              "      <td id=\"T_c025c_row20_col7\" class=\"data row20 col7\" >Falsely Negative</td>\n",
              "      <td id=\"T_c025c_row20_col8\" class=\"data row20 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row21\" class=\"row_heading level0 row21\" >TM6H4KLS</th>\n",
              "      <td id=\"T_c025c_row21_col0\" class=\"data row21 col0\" >Metropolis</td>\n",
              "      <td id=\"T_c025c_row21_col1\" class=\"data row21 col1\" >mixed</td>\n",
              "      <td id=\"T_c025c_row21_col2\" class=\"data row21 col2\" >negative</td>\n",
              "      <td id=\"T_c025c_row21_col3\" class=\"data row21 col3\" >negative</td>\n",
              "      <td id=\"T_c025c_row21_col4\" class=\"data row21 col4\" >-0.868100</td>\n",
              "      <td id=\"T_c025c_row21_col5\" class=\"data row21 col5\" >positive</td>\n",
              "      <td id=\"T_c025c_row21_col6\" class=\"data row21 col6\" >0.998000</td>\n",
              "      <td id=\"T_c025c_row21_col7\" class=\"data row21 col7\" >Correct</td>\n",
              "      <td id=\"T_c025c_row21_col8\" class=\"data row21 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row22\" class=\"row_heading level0 row22\" >8WNFN9AE</th>\n",
              "      <td id=\"T_c025c_row22_col0\" class=\"data row22 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_c025c_row22_col1\" class=\"data row22 col1\" >negative</td>\n",
              "      <td id=\"T_c025c_row22_col2\" class=\"data row22 col2\" >negative</td>\n",
              "      <td id=\"T_c025c_row22_col3\" class=\"data row22 col3\" >negative</td>\n",
              "      <td id=\"T_c025c_row22_col4\" class=\"data row22 col4\" >-0.971300</td>\n",
              "      <td id=\"T_c025c_row22_col5\" class=\"data row22 col5\" >positive</td>\n",
              "      <td id=\"T_c025c_row22_col6\" class=\"data row22 col6\" >0.869600</td>\n",
              "      <td id=\"T_c025c_row22_col7\" class=\"data row22 col7\" >Correct</td>\n",
              "      <td id=\"T_c025c_row22_col8\" class=\"data row22 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row23\" class=\"row_heading level0 row23\" >5YA546KU</th>\n",
              "      <td id=\"T_c025c_row23_col0\" class=\"data row23 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_c025c_row23_col1\" class=\"data row23 col1\" >mixed</td>\n",
              "      <td id=\"T_c025c_row23_col2\" class=\"data row23 col2\" >positive</td>\n",
              "      <td id=\"T_c025c_row23_col3\" class=\"data row23 col3\" >negative</td>\n",
              "      <td id=\"T_c025c_row23_col4\" class=\"data row23 col4\" >-0.880700</td>\n",
              "      <td id=\"T_c025c_row23_col5\" class=\"data row23 col5\" >positive</td>\n",
              "      <td id=\"T_c025c_row23_col6\" class=\"data row23 col6\" >0.666600</td>\n",
              "      <td id=\"T_c025c_row23_col7\" class=\"data row23 col7\" >Falsely Negative</td>\n",
              "      <td id=\"T_c025c_row23_col8\" class=\"data row23 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row24\" class=\"row_heading level0 row24\" >9W4Z3TFU</th>\n",
              "      <td id=\"T_c025c_row24_col0\" class=\"data row24 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_c025c_row24_col1\" class=\"data row24 col1\" >mixed</td>\n",
              "      <td id=\"T_c025c_row24_col2\" class=\"data row24 col2\" >positive</td>\n",
              "      <td id=\"T_c025c_row24_col3\" class=\"data row24 col3\" >positive</td>\n",
              "      <td id=\"T_c025c_row24_col4\" class=\"data row24 col4\" >0.928700</td>\n",
              "      <td id=\"T_c025c_row24_col5\" class=\"data row24 col5\" >negative</td>\n",
              "      <td id=\"T_c025c_row24_col6\" class=\"data row24 col6\" >-0.995600</td>\n",
              "      <td id=\"T_c025c_row24_col7\" class=\"data row24 col7\" >Correct</td>\n",
              "      <td id=\"T_c025c_row24_col8\" class=\"data row24 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row25\" class=\"row_heading level0 row25\" >K7EEHSC6</th>\n",
              "      <td id=\"T_c025c_row25_col0\" class=\"data row25 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_c025c_row25_col1\" class=\"data row25 col1\" >mixed</td>\n",
              "      <td id=\"T_c025c_row25_col2\" class=\"data row25 col2\" >positive</td>\n",
              "      <td id=\"T_c025c_row25_col3\" class=\"data row25 col3\" >positive</td>\n",
              "      <td id=\"T_c025c_row25_col4\" class=\"data row25 col4\" >0.931300</td>\n",
              "      <td id=\"T_c025c_row25_col5\" class=\"data row25 col5\" >negative</td>\n",
              "      <td id=\"T_c025c_row25_col6\" class=\"data row25 col6\" >-0.891600</td>\n",
              "      <td id=\"T_c025c_row25_col7\" class=\"data row25 col7\" >Correct</td>\n",
              "      <td id=\"T_c025c_row25_col8\" class=\"data row25 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row26\" class=\"row_heading level0 row26\" >CEHEIWR7</th>\n",
              "      <td id=\"T_c025c_row26_col0\" class=\"data row26 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_c025c_row26_col1\" class=\"data row26 col1\" >mixed</td>\n",
              "      <td id=\"T_c025c_row26_col2\" class=\"data row26 col2\" >positive</td>\n",
              "      <td id=\"T_c025c_row26_col3\" class=\"data row26 col3\" >negative</td>\n",
              "      <td id=\"T_c025c_row26_col4\" class=\"data row26 col4\" >-0.911800</td>\n",
              "      <td id=\"T_c025c_row26_col5\" class=\"data row26 col5\" >positive</td>\n",
              "      <td id=\"T_c025c_row26_col6\" class=\"data row26 col6\" >0.578900</td>\n",
              "      <td id=\"T_c025c_row26_col7\" class=\"data row26 col7\" >Falsely Negative</td>\n",
              "      <td id=\"T_c025c_row26_col8\" class=\"data row26 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row27\" class=\"row_heading level0 row27\" >2SQ2YDPA</th>\n",
              "      <td id=\"T_c025c_row27_col0\" class=\"data row27 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_c025c_row27_col1\" class=\"data row27 col1\" >mixed</td>\n",
              "      <td id=\"T_c025c_row27_col2\" class=\"data row27 col2\" >positive</td>\n",
              "      <td id=\"T_c025c_row27_col3\" class=\"data row27 col3\" >positive</td>\n",
              "      <td id=\"T_c025c_row27_col4\" class=\"data row27 col4\" >0.739400</td>\n",
              "      <td id=\"T_c025c_row27_col5\" class=\"data row27 col5\" >negative</td>\n",
              "      <td id=\"T_c025c_row27_col6\" class=\"data row27 col6\" >-0.980200</td>\n",
              "      <td id=\"T_c025c_row27_col7\" class=\"data row27 col7\" >Correct</td>\n",
              "      <td id=\"T_c025c_row27_col8\" class=\"data row27 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row28\" class=\"row_heading level0 row28\" >KWC9KUTP</th>\n",
              "      <td id=\"T_c025c_row28_col0\" class=\"data row28 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_c025c_row28_col1\" class=\"data row28 col1\" >negative</td>\n",
              "      <td id=\"T_c025c_row28_col2\" class=\"data row28 col2\" >negative</td>\n",
              "      <td id=\"T_c025c_row28_col3\" class=\"data row28 col3\" >negative</td>\n",
              "      <td id=\"T_c025c_row28_col4\" class=\"data row28 col4\" >-0.755500</td>\n",
              "      <td id=\"T_c025c_row28_col5\" class=\"data row28 col5\" >positive</td>\n",
              "      <td id=\"T_c025c_row28_col6\" class=\"data row28 col6\" >0.994100</td>\n",
              "      <td id=\"T_c025c_row28_col7\" class=\"data row28 col7\" >Correct</td>\n",
              "      <td id=\"T_c025c_row28_col8\" class=\"data row28 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c025c_level0_row29\" class=\"row_heading level0 row29\" >L7JBS7LN</th>\n",
              "      <td id=\"T_c025c_row29_col0\" class=\"data row29 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_c025c_row29_col1\" class=\"data row29 col1\" >positive</td>\n",
              "      <td id=\"T_c025c_row29_col2\" class=\"data row29 col2\" >positive</td>\n",
              "      <td id=\"T_c025c_row29_col3\" class=\"data row29 col3\" >positive</td>\n",
              "      <td id=\"T_c025c_row29_col4\" class=\"data row29 col4\" >0.943300</td>\n",
              "      <td id=\"T_c025c_row29_col5\" class=\"data row29 col5\" >negative</td>\n",
              "      <td id=\"T_c025c_row29_col6\" class=\"data row29 col6\" >-0.969200</td>\n",
              "      <td id=\"T_c025c_row29_col7\" class=\"data row29 col7\" >Correct</td>\n",
              "      <td id=\"T_c025c_row29_col8\" class=\"data row29 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Let's get subsets of this table per movie"
      ],
      "metadata": {
        "id": "gHUjmCeAOEqj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Caligari heat map"
      ],
      "metadata": {
        "id": "389CzkESOKhM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Select caligari reviews only\n",
        "caligari_wrong = divergent[(divergent['Movie'] == 'Caligari')]"
      ],
      "metadata": {
        "id": "HilxwBoJPnC4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply the custom function to style \"caligari_wrong\"\n",
        "caligari_wrong_map = caligari_wrong.style.applymap(map_sentiment_to_color, subset=['Manual_Judgment', 'Binary_Judgment', 'VADER_review_sentiment', 'VADER_ChatGPT_sentiment'])\\\n",
        "    .set_properties(**{\n",
        "        'text-align': 'center',\n",
        "        'font-size': '16px'  # You can adjust the font size as desired\n",
        "    })\\\n",
        "    .background_gradient(cmap=cmapR, subset=['VADER_review_score', 'VADER_ChatGPT_score'], vmin=-1, vmax=1)\\\n",
        "    .applymap(map_errors_to_color, subset=['GPT_Error_Check', 'VADER_Error_Check'])\n",
        "\n",
        "# Save the styled DataFrame to an HTML file\n",
        "caligari_wrong_map.to_html('caligari_wrong_map.html')"
      ],
      "metadata": {
        "id": "bBk8GvYzxArt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "caligari_wrong_map"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "Tolgxw-2QQm8",
        "outputId": "62dd41cf-aa04-444f-d33c-851d638f52ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7c7c2ac0d4e0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_c60e2_row0_col0, #T_c60e2_row1_col0, #T_c60e2_row2_col0, #T_c60e2_row3_col0, #T_c60e2_row4_col0, #T_c60e2_row5_col0, #T_c60e2_row6_col0, #T_c60e2_row7_col0, #T_c60e2_row8_col0, #T_c60e2_row9_col0 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "}\n",
              "#T_c60e2_row0_col1, #T_c60e2_row0_col2, #T_c60e2_row0_col3, #T_c60e2_row1_col5, #T_c60e2_row2_col1, #T_c60e2_row2_col2, #T_c60e2_row2_col3, #T_c60e2_row3_col1, #T_c60e2_row3_col2, #T_c60e2_row4_col5, #T_c60e2_row5_col1, #T_c60e2_row5_col2, #T_c60e2_row5_col3, #T_c60e2_row6_col5, #T_c60e2_row7_col3, #T_c60e2_row7_col5, #T_c60e2_row8_col5, #T_c60e2_row9_col5 {\n",
              "  background-color: firebrick;\n",
              "  color: white;\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "}\n",
              "#T_c60e2_row0_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #f26841;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c60e2_row0_col5, #T_c60e2_row1_col1, #T_c60e2_row1_col2, #T_c60e2_row1_col3, #T_c60e2_row2_col5, #T_c60e2_row3_col3, #T_c60e2_row3_col5, #T_c60e2_row4_col2, #T_c60e2_row4_col3, #T_c60e2_row5_col5, #T_c60e2_row6_col1, #T_c60e2_row6_col2, #T_c60e2_row6_col3, #T_c60e2_row7_col1, #T_c60e2_row7_col2, #T_c60e2_row8_col1, #T_c60e2_row8_col2, #T_c60e2_row8_col3, #T_c60e2_row9_col1, #T_c60e2_row9_col2, #T_c60e2_row9_col3 {\n",
              "  background-color: darkgreen;\n",
              "  color: white;\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "}\n",
              "#T_c60e2_row0_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #006837;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c60e2_row0_col7, #T_c60e2_row1_col7, #T_c60e2_row2_col7, #T_c60e2_row4_col7, #T_c60e2_row5_col7, #T_c60e2_row6_col7, #T_c60e2_row8_col7, #T_c60e2_row9_col7 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: darkgreen;\n",
              "  color: white;\n",
              "}\n",
              "#T_c60e2_row0_col8, #T_c60e2_row2_col8, #T_c60e2_row3_col7, #T_c60e2_row3_col8, #T_c60e2_row5_col8 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: white;\n",
              "  color: darkgreen;\n",
              "}\n",
              "#T_c60e2_row1_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #026c39;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c60e2_row1_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #f7814c;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c60e2_row1_col8, #T_c60e2_row4_col8, #T_c60e2_row6_col8, #T_c60e2_row7_col7, #T_c60e2_row7_col8, #T_c60e2_row8_col8, #T_c60e2_row9_col8 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: white;\n",
              "  color: firebrick;\n",
              "}\n",
              "#T_c60e2_row2_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #b91326;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c60e2_row2_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #c7e77f;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_c60e2_row3_col4, #T_c60e2_row8_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #036e3a;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c60e2_row3_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #96d268;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_c60e2_row4_col1 {\n",
              "  background-color: gold;\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "}\n",
              "#T_c60e2_row4_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #05713c;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c60e2_row4_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #ad0826;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c60e2_row5_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #e65036;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c60e2_row5_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #0e8245;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c60e2_row6_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #0a7b41;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c60e2_row6_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #c62027;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c60e2_row7_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #fdb768;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_c60e2_row7_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #a70226;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c60e2_row8_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #f57245;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_c60e2_row9_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #7ac665;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_c60e2_row9_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #a50026;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_c60e2\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_c60e2_level0_col0\" class=\"col_heading level0 col0\" >Movie</th>\n",
              "      <th id=\"T_c60e2_level0_col1\" class=\"col_heading level0 col1\" >Manual_Judgment</th>\n",
              "      <th id=\"T_c60e2_level0_col2\" class=\"col_heading level0 col2\" >Binary_Judgment</th>\n",
              "      <th id=\"T_c60e2_level0_col3\" class=\"col_heading level0 col3\" >VADER_ChatGPT_sentiment</th>\n",
              "      <th id=\"T_c60e2_level0_col4\" class=\"col_heading level0 col4\" >VADER_ChatGPT_score</th>\n",
              "      <th id=\"T_c60e2_level0_col5\" class=\"col_heading level0 col5\" >VADER_review_sentiment</th>\n",
              "      <th id=\"T_c60e2_level0_col6\" class=\"col_heading level0 col6\" >VADER_review_score</th>\n",
              "      <th id=\"T_c60e2_level0_col7\" class=\"col_heading level0 col7\" >GPT_Error_Check</th>\n",
              "      <th id=\"T_c60e2_level0_col8\" class=\"col_heading level0 col8\" >VADER_Error_Check</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >ID</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "      <th class=\"blank col6\" >&nbsp;</th>\n",
              "      <th class=\"blank col7\" >&nbsp;</th>\n",
              "      <th class=\"blank col8\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_c60e2_level0_row0\" class=\"row_heading level0 row0\" >MXZKVQ6B</th>\n",
              "      <td id=\"T_c60e2_row0_col0\" class=\"data row0 col0\" >Caligari</td>\n",
              "      <td id=\"T_c60e2_row0_col1\" class=\"data row0 col1\" >negative</td>\n",
              "      <td id=\"T_c60e2_row0_col2\" class=\"data row0 col2\" >negative</td>\n",
              "      <td id=\"T_c60e2_row0_col3\" class=\"data row0 col3\" >negative</td>\n",
              "      <td id=\"T_c60e2_row0_col4\" class=\"data row0 col4\" >-0.610300</td>\n",
              "      <td id=\"T_c60e2_row0_col5\" class=\"data row0 col5\" >positive</td>\n",
              "      <td id=\"T_c60e2_row0_col6\" class=\"data row0 col6\" >0.997500</td>\n",
              "      <td id=\"T_c60e2_row0_col7\" class=\"data row0 col7\" >Correct</td>\n",
              "      <td id=\"T_c60e2_row0_col8\" class=\"data row0 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c60e2_level0_row1\" class=\"row_heading level0 row1\" >G2XQWE7E</th>\n",
              "      <td id=\"T_c60e2_row1_col0\" class=\"data row1 col0\" >Caligari</td>\n",
              "      <td id=\"T_c60e2_row1_col1\" class=\"data row1 col1\" >positive</td>\n",
              "      <td id=\"T_c60e2_row1_col2\" class=\"data row1 col2\" >positive</td>\n",
              "      <td id=\"T_c60e2_row1_col3\" class=\"data row1 col3\" >positive</td>\n",
              "      <td id=\"T_c60e2_row1_col4\" class=\"data row1 col4\" >0.984200</td>\n",
              "      <td id=\"T_c60e2_row1_col5\" class=\"data row1 col5\" >negative</td>\n",
              "      <td id=\"T_c60e2_row1_col6\" class=\"data row1 col6\" >-0.538700</td>\n",
              "      <td id=\"T_c60e2_row1_col7\" class=\"data row1 col7\" >Correct</td>\n",
              "      <td id=\"T_c60e2_row1_col8\" class=\"data row1 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c60e2_level0_row2\" class=\"row_heading level0 row2\" >LU2I7PCZ</th>\n",
              "      <td id=\"T_c60e2_row2_col0\" class=\"data row2 col0\" >Caligari</td>\n",
              "      <td id=\"T_c60e2_row2_col1\" class=\"data row2 col1\" >negative</td>\n",
              "      <td id=\"T_c60e2_row2_col2\" class=\"data row2 col2\" >negative</td>\n",
              "      <td id=\"T_c60e2_row2_col3\" class=\"data row2 col3\" >negative</td>\n",
              "      <td id=\"T_c60e2_row2_col4\" class=\"data row2 col4\" >-0.916900</td>\n",
              "      <td id=\"T_c60e2_row2_col5\" class=\"data row2 col5\" >positive</td>\n",
              "      <td id=\"T_c60e2_row2_col6\" class=\"data row2 col6\" >0.270900</td>\n",
              "      <td id=\"T_c60e2_row2_col7\" class=\"data row2 col7\" >Correct</td>\n",
              "      <td id=\"T_c60e2_row2_col8\" class=\"data row2 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c60e2_level0_row3\" class=\"row_heading level0 row3\" >PJSEZGQ4</th>\n",
              "      <td id=\"T_c60e2_row3_col0\" class=\"data row3 col0\" >Caligari</td>\n",
              "      <td id=\"T_c60e2_row3_col1\" class=\"data row3 col1\" >negative</td>\n",
              "      <td id=\"T_c60e2_row3_col2\" class=\"data row3 col2\" >negative</td>\n",
              "      <td id=\"T_c60e2_row3_col3\" class=\"data row3 col3\" >positive</td>\n",
              "      <td id=\"T_c60e2_row3_col4\" class=\"data row3 col4\" >0.971900</td>\n",
              "      <td id=\"T_c60e2_row3_col5\" class=\"data row3 col5\" >positive</td>\n",
              "      <td id=\"T_c60e2_row3_col6\" class=\"data row3 col6\" >0.452700</td>\n",
              "      <td id=\"T_c60e2_row3_col7\" class=\"data row3 col7\" >Falsely Positive</td>\n",
              "      <td id=\"T_c60e2_row3_col8\" class=\"data row3 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c60e2_level0_row4\" class=\"row_heading level0 row4\" >FKFS95J3</th>\n",
              "      <td id=\"T_c60e2_row4_col0\" class=\"data row4 col0\" >Caligari</td>\n",
              "      <td id=\"T_c60e2_row4_col1\" class=\"data row4 col1\" >mixed</td>\n",
              "      <td id=\"T_c60e2_row4_col2\" class=\"data row4 col2\" >positive</td>\n",
              "      <td id=\"T_c60e2_row4_col3\" class=\"data row4 col3\" >positive</td>\n",
              "      <td id=\"T_c60e2_row4_col4\" class=\"data row4 col4\" >0.956500</td>\n",
              "      <td id=\"T_c60e2_row4_col5\" class=\"data row4 col5\" >negative</td>\n",
              "      <td id=\"T_c60e2_row4_col6\" class=\"data row4 col6\" >-0.963100</td>\n",
              "      <td id=\"T_c60e2_row4_col7\" class=\"data row4 col7\" >Correct</td>\n",
              "      <td id=\"T_c60e2_row4_col8\" class=\"data row4 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c60e2_level0_row5\" class=\"row_heading level0 row5\" >WYMPWWPX</th>\n",
              "      <td id=\"T_c60e2_row5_col0\" class=\"data row5 col0\" >Caligari</td>\n",
              "      <td id=\"T_c60e2_row5_col1\" class=\"data row5 col1\" >negative</td>\n",
              "      <td id=\"T_c60e2_row5_col2\" class=\"data row5 col2\" >negative</td>\n",
              "      <td id=\"T_c60e2_row5_col3\" class=\"data row5 col3\" >negative</td>\n",
              "      <td id=\"T_c60e2_row5_col4\" class=\"data row5 col4\" >-0.694300</td>\n",
              "      <td id=\"T_c60e2_row5_col5\" class=\"data row5 col5\" >positive</td>\n",
              "      <td id=\"T_c60e2_row5_col6\" class=\"data row5 col6\" >0.889200</td>\n",
              "      <td id=\"T_c60e2_row5_col7\" class=\"data row5 col7\" >Correct</td>\n",
              "      <td id=\"T_c60e2_row5_col8\" class=\"data row5 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c60e2_level0_row6\" class=\"row_heading level0 row6\" >INTVKS32</th>\n",
              "      <td id=\"T_c60e2_row6_col0\" class=\"data row6 col0\" >Caligari</td>\n",
              "      <td id=\"T_c60e2_row6_col1\" class=\"data row6 col1\" >positive</td>\n",
              "      <td id=\"T_c60e2_row6_col2\" class=\"data row6 col2\" >positive</td>\n",
              "      <td id=\"T_c60e2_row6_col3\" class=\"data row6 col3\" >positive</td>\n",
              "      <td id=\"T_c60e2_row6_col4\" class=\"data row6 col4\" >0.917900</td>\n",
              "      <td id=\"T_c60e2_row6_col5\" class=\"data row6 col5\" >negative</td>\n",
              "      <td id=\"T_c60e2_row6_col6\" class=\"data row6 col6\" >-0.860600</td>\n",
              "      <td id=\"T_c60e2_row6_col7\" class=\"data row6 col7\" >Correct</td>\n",
              "      <td id=\"T_c60e2_row6_col8\" class=\"data row6 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c60e2_level0_row7\" class=\"row_heading level0 row7\" >VNXHI9SG</th>\n",
              "      <td id=\"T_c60e2_row7_col0\" class=\"data row7 col0\" >Caligari</td>\n",
              "      <td id=\"T_c60e2_row7_col1\" class=\"data row7 col1\" >positive</td>\n",
              "      <td id=\"T_c60e2_row7_col2\" class=\"data row7 col2\" >positive</td>\n",
              "      <td id=\"T_c60e2_row7_col3\" class=\"data row7 col3\" >negative</td>\n",
              "      <td id=\"T_c60e2_row7_col4\" class=\"data row7 col4\" >-0.361200</td>\n",
              "      <td id=\"T_c60e2_row7_col5\" class=\"data row7 col5\" >negative</td>\n",
              "      <td id=\"T_c60e2_row7_col6\" class=\"data row7 col6\" >-0.986100</td>\n",
              "      <td id=\"T_c60e2_row7_col7\" class=\"data row7 col7\" >Falsely Negative</td>\n",
              "      <td id=\"T_c60e2_row7_col8\" class=\"data row7 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c60e2_level0_row8\" class=\"row_heading level0 row8\" >JDMUZ7Z7</th>\n",
              "      <td id=\"T_c60e2_row8_col0\" class=\"data row8 col0\" >Caligari</td>\n",
              "      <td id=\"T_c60e2_row8_col1\" class=\"data row8 col1\" >positive</td>\n",
              "      <td id=\"T_c60e2_row8_col2\" class=\"data row8 col2\" >positive</td>\n",
              "      <td id=\"T_c60e2_row8_col3\" class=\"data row8 col3\" >positive</td>\n",
              "      <td id=\"T_c60e2_row8_col4\" class=\"data row8 col4\" >0.970500</td>\n",
              "      <td id=\"T_c60e2_row8_col5\" class=\"data row8 col5\" >negative</td>\n",
              "      <td id=\"T_c60e2_row8_col6\" class=\"data row8 col6\" >-0.579300</td>\n",
              "      <td id=\"T_c60e2_row8_col7\" class=\"data row8 col7\" >Correct</td>\n",
              "      <td id=\"T_c60e2_row8_col8\" class=\"data row8 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c60e2_level0_row9\" class=\"row_heading level0 row9\" >FWT8J86D</th>\n",
              "      <td id=\"T_c60e2_row9_col0\" class=\"data row9 col0\" >Caligari</td>\n",
              "      <td id=\"T_c60e2_row9_col1\" class=\"data row9 col1\" >positive</td>\n",
              "      <td id=\"T_c60e2_row9_col2\" class=\"data row9 col2\" >positive</td>\n",
              "      <td id=\"T_c60e2_row9_col3\" class=\"data row9 col3\" >positive</td>\n",
              "      <td id=\"T_c60e2_row9_col4\" class=\"data row9 col4\" >0.535800</td>\n",
              "      <td id=\"T_c60e2_row9_col5\" class=\"data row9 col5\" >negative</td>\n",
              "      <td id=\"T_c60e2_row9_col6\" class=\"data row9 col6\" >-0.995500</td>\n",
              "      <td id=\"T_c60e2_row9_col7\" class=\"data row9 col7\" >Correct</td>\n",
              "      <td id=\"T_c60e2_row9_col8\" class=\"data row9 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Metropolis heat map"
      ],
      "metadata": {
        "id": "ioXQdm4SQach"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Select metropolis reviews only\n",
        "metropolis_wrong = divergent[(divergent['Movie'] == 'Metropolis')]"
      ],
      "metadata": {
        "id": "dNA1xkPBQach"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply the custom function to style \"metropolis_wrong\"\n",
        "metropolis_wrong_map = metropolis_wrong.style.applymap(map_sentiment_to_color, subset=['Manual_Judgment', 'Binary_Judgment', 'VADER_review_sentiment', 'VADER_ChatGPT_sentiment'])\\\n",
        "    .set_properties(**{\n",
        "        'text-align': 'center',\n",
        "        'font-size': '16px'  # You can adjust the font size as desired\n",
        "    })\\\n",
        "    .background_gradient(cmap=cmapR, subset=['VADER_review_score', 'VADER_ChatGPT_score'], vmin=-1, vmax=1)\\\n",
        "    .applymap(map_errors_to_color, subset=['GPT_Error_Check', 'VADER_Error_Check'])\n",
        "\n",
        "# Save the styled DataFrame to an HTML file\n",
        "metropolis_wrong_map.to_html('metropolis_wrong_map.html')"
      ],
      "metadata": {
        "id": "pt47FihkxRwS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "metropolis_wrong_map"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "outputId": "9e261e13-7603-41ab-8c4c-2f0db7ba96d9",
        "id": "uIKhR96LQaci"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7c7c2ac0dff0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_bcde9_row0_col0, #T_bcde9_row1_col0, #T_bcde9_row2_col0, #T_bcde9_row3_col0, #T_bcde9_row4_col0, #T_bcde9_row5_col0, #T_bcde9_row6_col0, #T_bcde9_row7_col0, #T_bcde9_row8_col0, #T_bcde9_row9_col0, #T_bcde9_row10_col0, #T_bcde9_row11_col0 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "}\n",
              "#T_bcde9_row0_col1, #T_bcde9_row1_col1, #T_bcde9_row2_col1, #T_bcde9_row4_col1, #T_bcde9_row4_col3, #T_bcde9_row5_col1, #T_bcde9_row8_col1, #T_bcde9_row10_col1, #T_bcde9_row11_col1 {\n",
              "  background-color: gold;\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "}\n",
              "#T_bcde9_row0_col2, #T_bcde9_row1_col2, #T_bcde9_row2_col2, #T_bcde9_row2_col3, #T_bcde9_row3_col1, #T_bcde9_row3_col2, #T_bcde9_row3_col3, #T_bcde9_row4_col2, #T_bcde9_row5_col2, #T_bcde9_row6_col1, #T_bcde9_row6_col2, #T_bcde9_row6_col3, #T_bcde9_row7_col1, #T_bcde9_row7_col2, #T_bcde9_row7_col3, #T_bcde9_row8_col2, #T_bcde9_row8_col3, #T_bcde9_row9_col1, #T_bcde9_row9_col2, #T_bcde9_row10_col3, #T_bcde9_row11_col2, #T_bcde9_row11_col3 {\n",
              "  background-color: firebrick;\n",
              "  color: white;\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "}\n",
              "#T_bcde9_row0_col3, #T_bcde9_row0_col5, #T_bcde9_row1_col3, #T_bcde9_row1_col5, #T_bcde9_row2_col5, #T_bcde9_row3_col5, #T_bcde9_row4_col5, #T_bcde9_row5_col3, #T_bcde9_row5_col5, #T_bcde9_row6_col5, #T_bcde9_row7_col5, #T_bcde9_row8_col5, #T_bcde9_row9_col3, #T_bcde9_row9_col5, #T_bcde9_row10_col2, #T_bcde9_row10_col5, #T_bcde9_row11_col5 {\n",
              "  background-color: darkgreen;\n",
              "  color: white;\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "}\n",
              "#T_bcde9_row0_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #84ca66;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_bcde9_row0_col6, #T_bcde9_row9_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #026c39;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_bcde9_row0_col7, #T_bcde9_row0_col8, #T_bcde9_row1_col7, #T_bcde9_row1_col8, #T_bcde9_row2_col8, #T_bcde9_row3_col8, #T_bcde9_row4_col8, #T_bcde9_row5_col7, #T_bcde9_row5_col8, #T_bcde9_row6_col8, #T_bcde9_row7_col8, #T_bcde9_row8_col8, #T_bcde9_row9_col7, #T_bcde9_row9_col8, #T_bcde9_row11_col8 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: white;\n",
              "  color: darkgreen;\n",
              "}\n",
              "#T_bcde9_row1_col4, #T_bcde9_row5_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #7fc866;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_bcde9_row1_col6, #T_bcde9_row3_col6, #T_bcde9_row5_col6, #T_bcde9_row7_col6, #T_bcde9_row8_col6, #T_bcde9_row9_col6, #T_bcde9_row10_col6, #T_bcde9_row11_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #006837;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_bcde9_row2_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #de402e;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_bcde9_row2_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #016a38;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_bcde9_row2_col7, #T_bcde9_row3_col7, #T_bcde9_row4_col7, #T_bcde9_row6_col7, #T_bcde9_row7_col7, #T_bcde9_row8_col7, #T_bcde9_row10_col8, #T_bcde9_row11_col7 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: darkgreen;\n",
              "  color: white;\n",
              "}\n",
              "#T_bcde9_row3_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #af0926;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_bcde9_row4_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #fff5ae;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_bcde9_row4_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #097940;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_bcde9_row6_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #ce2827;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_bcde9_row6_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #0c7f43;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_bcde9_row7_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #b91326;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_bcde9_row8_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #bd1726;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_bcde9_row10_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #e54e35;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_bcde9_row10_col7 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: white;\n",
              "  color: firebrick;\n",
              "}\n",
              "#T_bcde9_row11_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #c41e27;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_bcde9\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_bcde9_level0_col0\" class=\"col_heading level0 col0\" >Movie</th>\n",
              "      <th id=\"T_bcde9_level0_col1\" class=\"col_heading level0 col1\" >Manual_Judgment</th>\n",
              "      <th id=\"T_bcde9_level0_col2\" class=\"col_heading level0 col2\" >Binary_Judgment</th>\n",
              "      <th id=\"T_bcde9_level0_col3\" class=\"col_heading level0 col3\" >VADER_ChatGPT_sentiment</th>\n",
              "      <th id=\"T_bcde9_level0_col4\" class=\"col_heading level0 col4\" >VADER_ChatGPT_score</th>\n",
              "      <th id=\"T_bcde9_level0_col5\" class=\"col_heading level0 col5\" >VADER_review_sentiment</th>\n",
              "      <th id=\"T_bcde9_level0_col6\" class=\"col_heading level0 col6\" >VADER_review_score</th>\n",
              "      <th id=\"T_bcde9_level0_col7\" class=\"col_heading level0 col7\" >GPT_Error_Check</th>\n",
              "      <th id=\"T_bcde9_level0_col8\" class=\"col_heading level0 col8\" >VADER_Error_Check</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >ID</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "      <th class=\"blank col6\" >&nbsp;</th>\n",
              "      <th class=\"blank col7\" >&nbsp;</th>\n",
              "      <th class=\"blank col8\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_bcde9_level0_row0\" class=\"row_heading level0 row0\" >KIUF2ZGM</th>\n",
              "      <td id=\"T_bcde9_row0_col0\" class=\"data row0 col0\" >Metropolis</td>\n",
              "      <td id=\"T_bcde9_row0_col1\" class=\"data row0 col1\" >mixed</td>\n",
              "      <td id=\"T_bcde9_row0_col2\" class=\"data row0 col2\" >negative</td>\n",
              "      <td id=\"T_bcde9_row0_col3\" class=\"data row0 col3\" >positive</td>\n",
              "      <td id=\"T_bcde9_row0_col4\" class=\"data row0 col4\" >0.502300</td>\n",
              "      <td id=\"T_bcde9_row0_col5\" class=\"data row0 col5\" >positive</td>\n",
              "      <td id=\"T_bcde9_row0_col6\" class=\"data row0 col6\" >0.981100</td>\n",
              "      <td id=\"T_bcde9_row0_col7\" class=\"data row0 col7\" >Falsely Positive</td>\n",
              "      <td id=\"T_bcde9_row0_col8\" class=\"data row0 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bcde9_level0_row1\" class=\"row_heading level0 row1\" >EIRLBFNR</th>\n",
              "      <td id=\"T_bcde9_row1_col0\" class=\"data row1 col0\" >Metropolis</td>\n",
              "      <td id=\"T_bcde9_row1_col1\" class=\"data row1 col1\" >mixed</td>\n",
              "      <td id=\"T_bcde9_row1_col2\" class=\"data row1 col2\" >negative</td>\n",
              "      <td id=\"T_bcde9_row1_col3\" class=\"data row1 col3\" >positive</td>\n",
              "      <td id=\"T_bcde9_row1_col4\" class=\"data row1 col4\" >0.519200</td>\n",
              "      <td id=\"T_bcde9_row1_col5\" class=\"data row1 col5\" >positive</td>\n",
              "      <td id=\"T_bcde9_row1_col6\" class=\"data row1 col6\" >0.993800</td>\n",
              "      <td id=\"T_bcde9_row1_col7\" class=\"data row1 col7\" >Falsely Positive</td>\n",
              "      <td id=\"T_bcde9_row1_col8\" class=\"data row1 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bcde9_level0_row2\" class=\"row_heading level0 row2\" >JUZY74XR</th>\n",
              "      <td id=\"T_bcde9_row2_col0\" class=\"data row2 col0\" >Metropolis</td>\n",
              "      <td id=\"T_bcde9_row2_col1\" class=\"data row2 col1\" >mixed</td>\n",
              "      <td id=\"T_bcde9_row2_col2\" class=\"data row2 col2\" >negative</td>\n",
              "      <td id=\"T_bcde9_row2_col3\" class=\"data row2 col3\" >negative</td>\n",
              "      <td id=\"T_bcde9_row2_col4\" class=\"data row2 col4\" >-0.745400</td>\n",
              "      <td id=\"T_bcde9_row2_col5\" class=\"data row2 col5\" >positive</td>\n",
              "      <td id=\"T_bcde9_row2_col6\" class=\"data row2 col6\" >0.987700</td>\n",
              "      <td id=\"T_bcde9_row2_col7\" class=\"data row2 col7\" >Correct</td>\n",
              "      <td id=\"T_bcde9_row2_col8\" class=\"data row2 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bcde9_level0_row3\" class=\"row_heading level0 row3\" >KCDMVU3Q</th>\n",
              "      <td id=\"T_bcde9_row3_col0\" class=\"data row3 col0\" >Metropolis</td>\n",
              "      <td id=\"T_bcde9_row3_col1\" class=\"data row3 col1\" >negative</td>\n",
              "      <td id=\"T_bcde9_row3_col2\" class=\"data row3 col2\" >negative</td>\n",
              "      <td id=\"T_bcde9_row3_col3\" class=\"data row3 col3\" >negative</td>\n",
              "      <td id=\"T_bcde9_row3_col4\" class=\"data row3 col4\" >-0.955200</td>\n",
              "      <td id=\"T_bcde9_row3_col5\" class=\"data row3 col5\" >positive</td>\n",
              "      <td id=\"T_bcde9_row3_col6\" class=\"data row3 col6\" >0.996000</td>\n",
              "      <td id=\"T_bcde9_row3_col7\" class=\"data row3 col7\" >Correct</td>\n",
              "      <td id=\"T_bcde9_row3_col8\" class=\"data row3 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bcde9_level0_row4\" class=\"row_heading level0 row4\" >TIRHYW52</th>\n",
              "      <td id=\"T_bcde9_row4_col0\" class=\"data row4 col0\" >Metropolis</td>\n",
              "      <td id=\"T_bcde9_row4_col1\" class=\"data row4 col1\" >mixed</td>\n",
              "      <td id=\"T_bcde9_row4_col2\" class=\"data row4 col2\" >negative</td>\n",
              "      <td id=\"T_bcde9_row4_col3\" class=\"data row4 col3\" >mixed</td>\n",
              "      <td id=\"T_bcde9_row4_col4\" class=\"data row4 col4\" >-0.063400</td>\n",
              "      <td id=\"T_bcde9_row4_col5\" class=\"data row4 col5\" >positive</td>\n",
              "      <td id=\"T_bcde9_row4_col6\" class=\"data row4 col6\" >0.927400</td>\n",
              "      <td id=\"T_bcde9_row4_col7\" class=\"data row4 col7\" >Correct</td>\n",
              "      <td id=\"T_bcde9_row4_col8\" class=\"data row4 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bcde9_level0_row5\" class=\"row_heading level0 row5\" >AXSMZ7XF</th>\n",
              "      <td id=\"T_bcde9_row5_col0\" class=\"data row5 col0\" >Metropolis</td>\n",
              "      <td id=\"T_bcde9_row5_col1\" class=\"data row5 col1\" >mixed</td>\n",
              "      <td id=\"T_bcde9_row5_col2\" class=\"data row5 col2\" >negative</td>\n",
              "      <td id=\"T_bcde9_row5_col3\" class=\"data row5 col3\" >positive</td>\n",
              "      <td id=\"T_bcde9_row5_col4\" class=\"data row5 col4\" >0.518700</td>\n",
              "      <td id=\"T_bcde9_row5_col5\" class=\"data row5 col5\" >positive</td>\n",
              "      <td id=\"T_bcde9_row5_col6\" class=\"data row5 col6\" >0.992400</td>\n",
              "      <td id=\"T_bcde9_row5_col7\" class=\"data row5 col7\" >Falsely Positive</td>\n",
              "      <td id=\"T_bcde9_row5_col8\" class=\"data row5 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bcde9_level0_row6\" class=\"row_heading level0 row6\" >C5HFH5UD</th>\n",
              "      <td id=\"T_bcde9_row6_col0\" class=\"data row6 col0\" >Metropolis</td>\n",
              "      <td id=\"T_bcde9_row6_col1\" class=\"data row6 col1\" >negative</td>\n",
              "      <td id=\"T_bcde9_row6_col2\" class=\"data row6 col2\" >negative</td>\n",
              "      <td id=\"T_bcde9_row6_col3\" class=\"data row6 col3\" >negative</td>\n",
              "      <td id=\"T_bcde9_row6_col4\" class=\"data row6 col4\" >-0.829600</td>\n",
              "      <td id=\"T_bcde9_row6_col5\" class=\"data row6 col5\" >positive</td>\n",
              "      <td id=\"T_bcde9_row6_col6\" class=\"data row6 col6\" >0.902700</td>\n",
              "      <td id=\"T_bcde9_row6_col7\" class=\"data row6 col7\" >Correct</td>\n",
              "      <td id=\"T_bcde9_row6_col8\" class=\"data row6 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bcde9_level0_row7\" class=\"row_heading level0 row7\" >5KUVWUIP</th>\n",
              "      <td id=\"T_bcde9_row7_col0\" class=\"data row7 col0\" >Metropolis</td>\n",
              "      <td id=\"T_bcde9_row7_col1\" class=\"data row7 col1\" >negative</td>\n",
              "      <td id=\"T_bcde9_row7_col2\" class=\"data row7 col2\" >negative</td>\n",
              "      <td id=\"T_bcde9_row7_col3\" class=\"data row7 col3\" >negative</td>\n",
              "      <td id=\"T_bcde9_row7_col4\" class=\"data row7 col4\" >-0.920100</td>\n",
              "      <td id=\"T_bcde9_row7_col5\" class=\"data row7 col5\" >positive</td>\n",
              "      <td id=\"T_bcde9_row7_col6\" class=\"data row7 col6\" >0.995100</td>\n",
              "      <td id=\"T_bcde9_row7_col7\" class=\"data row7 col7\" >Correct</td>\n",
              "      <td id=\"T_bcde9_row7_col8\" class=\"data row7 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bcde9_level0_row8\" class=\"row_heading level0 row8\" >ZV2KYKDS</th>\n",
              "      <td id=\"T_bcde9_row8_col0\" class=\"data row8 col0\" >Metropolis</td>\n",
              "      <td id=\"T_bcde9_row8_col1\" class=\"data row8 col1\" >mixed</td>\n",
              "      <td id=\"T_bcde9_row8_col2\" class=\"data row8 col2\" >negative</td>\n",
              "      <td id=\"T_bcde9_row8_col3\" class=\"data row8 col3\" >negative</td>\n",
              "      <td id=\"T_bcde9_row8_col4\" class=\"data row8 col4\" >-0.901300</td>\n",
              "      <td id=\"T_bcde9_row8_col5\" class=\"data row8 col5\" >positive</td>\n",
              "      <td id=\"T_bcde9_row8_col6\" class=\"data row8 col6\" >0.997700</td>\n",
              "      <td id=\"T_bcde9_row8_col7\" class=\"data row8 col7\" >Correct</td>\n",
              "      <td id=\"T_bcde9_row8_col8\" class=\"data row8 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bcde9_level0_row9\" class=\"row_heading level0 row9\" >D4AR9KYS</th>\n",
              "      <td id=\"T_bcde9_row9_col0\" class=\"data row9 col0\" >Metropolis</td>\n",
              "      <td id=\"T_bcde9_row9_col1\" class=\"data row9 col1\" >negative</td>\n",
              "      <td id=\"T_bcde9_row9_col2\" class=\"data row9 col2\" >negative</td>\n",
              "      <td id=\"T_bcde9_row9_col3\" class=\"data row9 col3\" >positive</td>\n",
              "      <td id=\"T_bcde9_row9_col4\" class=\"data row9 col4\" >0.981000</td>\n",
              "      <td id=\"T_bcde9_row9_col5\" class=\"data row9 col5\" >positive</td>\n",
              "      <td id=\"T_bcde9_row9_col6\" class=\"data row9 col6\" >0.997800</td>\n",
              "      <td id=\"T_bcde9_row9_col7\" class=\"data row9 col7\" >Falsely Positive</td>\n",
              "      <td id=\"T_bcde9_row9_col8\" class=\"data row9 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bcde9_level0_row10\" class=\"row_heading level0 row10\" >CZWZ5UV7</th>\n",
              "      <td id=\"T_bcde9_row10_col0\" class=\"data row10 col0\" >Metropolis</td>\n",
              "      <td id=\"T_bcde9_row10_col1\" class=\"data row10 col1\" >mixed</td>\n",
              "      <td id=\"T_bcde9_row10_col2\" class=\"data row10 col2\" >positive</td>\n",
              "      <td id=\"T_bcde9_row10_col3\" class=\"data row10 col3\" >negative</td>\n",
              "      <td id=\"T_bcde9_row10_col4\" class=\"data row10 col4\" >-0.700300</td>\n",
              "      <td id=\"T_bcde9_row10_col5\" class=\"data row10 col5\" >positive</td>\n",
              "      <td id=\"T_bcde9_row10_col6\" class=\"data row10 col6\" >0.996100</td>\n",
              "      <td id=\"T_bcde9_row10_col7\" class=\"data row10 col7\" >Falsely Negative</td>\n",
              "      <td id=\"T_bcde9_row10_col8\" class=\"data row10 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bcde9_level0_row11\" class=\"row_heading level0 row11\" >TM6H4KLS</th>\n",
              "      <td id=\"T_bcde9_row11_col0\" class=\"data row11 col0\" >Metropolis</td>\n",
              "      <td id=\"T_bcde9_row11_col1\" class=\"data row11 col1\" >mixed</td>\n",
              "      <td id=\"T_bcde9_row11_col2\" class=\"data row11 col2\" >negative</td>\n",
              "      <td id=\"T_bcde9_row11_col3\" class=\"data row11 col3\" >negative</td>\n",
              "      <td id=\"T_bcde9_row11_col4\" class=\"data row11 col4\" >-0.868100</td>\n",
              "      <td id=\"T_bcde9_row11_col5\" class=\"data row11 col5\" >positive</td>\n",
              "      <td id=\"T_bcde9_row11_col6\" class=\"data row11 col6\" >0.998000</td>\n",
              "      <td id=\"T_bcde9_row11_col7\" class=\"data row11 col7\" >Correct</td>\n",
              "      <td id=\"T_bcde9_row11_col8\" class=\"data row11 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Nosferatu heat map"
      ],
      "metadata": {
        "id": "S6AXYAD-Q2dQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Select nosferatu reviews only\n",
        "nosferatu_wrong = divergent[(divergent['Movie'] == 'Nosferatu')]"
      ],
      "metadata": {
        "id": "Uu1QSJanQ2dQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply the custom function to style \"nosferatu_wrong\"\n",
        "nosferatu_wrong_map = nosferatu_wrong.style.applymap(map_sentiment_to_color, subset=['Manual_Judgment', 'Binary_Judgment', 'VADER_review_sentiment', 'VADER_ChatGPT_sentiment'])\\\n",
        "    .set_properties(**{\n",
        "        'text-align': 'center',\n",
        "        'font-size': '16px'  # You can adjust the font size as desired\n",
        "    })\\\n",
        "    .background_gradient(cmap=cmapR, subset=['VADER_review_score', 'VADER_ChatGPT_score'], vmin=-1, vmax=1)\\\n",
        "    .applymap(map_errors_to_color, subset=['GPT_Error_Check', 'VADER_Error_Check'])\n",
        "\n",
        "# Save the styled DataFrame to an HTML file\n",
        "nosferatu_wrong_map.to_html('nosferatu_wrong_map.html')"
      ],
      "metadata": {
        "id": "MO7k-EZVxtZD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nosferatu_wrong_map"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "outputId": "389425e9-e9bf-4e85-85c9-8db423675cf1",
        "id": "jOf2O8JFQ2dR"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7c7c2ac0d870>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_b0bc2_row0_col0, #T_b0bc2_row1_col0, #T_b0bc2_row2_col0, #T_b0bc2_row3_col0, #T_b0bc2_row4_col0, #T_b0bc2_row5_col0, #T_b0bc2_row6_col0, #T_b0bc2_row7_col0 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "}\n",
              "#T_b0bc2_row0_col1, #T_b0bc2_row0_col2, #T_b0bc2_row0_col3, #T_b0bc2_row1_col3, #T_b0bc2_row2_col5, #T_b0bc2_row3_col5, #T_b0bc2_row4_col3, #T_b0bc2_row5_col5, #T_b0bc2_row6_col1, #T_b0bc2_row6_col2, #T_b0bc2_row6_col3, #T_b0bc2_row7_col5 {\n",
              "  background-color: firebrick;\n",
              "  color: white;\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "}\n",
              "#T_b0bc2_row0_col4, #T_b0bc2_row7_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #ab0626;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_b0bc2_row0_col5, #T_b0bc2_row1_col2, #T_b0bc2_row1_col5, #T_b0bc2_row2_col2, #T_b0bc2_row2_col3, #T_b0bc2_row3_col2, #T_b0bc2_row3_col3, #T_b0bc2_row4_col2, #T_b0bc2_row4_col5, #T_b0bc2_row5_col2, #T_b0bc2_row5_col3, #T_b0bc2_row6_col5, #T_b0bc2_row7_col1, #T_b0bc2_row7_col2, #T_b0bc2_row7_col3 {\n",
              "  background-color: darkgreen;\n",
              "  color: white;\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "}\n",
              "#T_b0bc2_row0_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #108647;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_b0bc2_row0_col7, #T_b0bc2_row1_col8, #T_b0bc2_row2_col7, #T_b0bc2_row3_col7, #T_b0bc2_row4_col8, #T_b0bc2_row5_col7, #T_b0bc2_row6_col7, #T_b0bc2_row7_col7 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: darkgreen;\n",
              "  color: white;\n",
              "}\n",
              "#T_b0bc2_row0_col8, #T_b0bc2_row6_col8 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: white;\n",
              "  color: darkgreen;\n",
              "}\n",
              "#T_b0bc2_row1_col1, #T_b0bc2_row2_col1, #T_b0bc2_row3_col1, #T_b0bc2_row4_col1, #T_b0bc2_row5_col1 {\n",
              "  background-color: gold;\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "}\n",
              "#T_b0bc2_row1_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #c21c27;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_b0bc2_row1_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #4bb05c;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_b0bc2_row1_col7, #T_b0bc2_row2_col8, #T_b0bc2_row3_col8, #T_b0bc2_row4_col7, #T_b0bc2_row5_col8, #T_b0bc2_row7_col8 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: white;\n",
              "  color: firebrick;\n",
              "}\n",
              "#T_b0bc2_row2_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #097940;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_b0bc2_row2_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #a50026;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_b0bc2_row3_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #08773f;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_b0bc2_row3_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #be1827;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_b0bc2_row4_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #bb1526;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_b0bc2_row4_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #6bbf64;\n",
              "  color: #000000;\n",
              "}\n",
              "#T_b0bc2_row5_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #30a356;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_b0bc2_row5_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #a90426;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_b0bc2_row6_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #dd3d2d;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_b0bc2_row6_col6 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #006837;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_b0bc2_row7_col4 {\n",
              "  text-align: center;\n",
              "  font-size: 16px;\n",
              "  background-color: #07753e;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_b0bc2\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_b0bc2_level0_col0\" class=\"col_heading level0 col0\" >Movie</th>\n",
              "      <th id=\"T_b0bc2_level0_col1\" class=\"col_heading level0 col1\" >Manual_Judgment</th>\n",
              "      <th id=\"T_b0bc2_level0_col2\" class=\"col_heading level0 col2\" >Binary_Judgment</th>\n",
              "      <th id=\"T_b0bc2_level0_col3\" class=\"col_heading level0 col3\" >VADER_ChatGPT_sentiment</th>\n",
              "      <th id=\"T_b0bc2_level0_col4\" class=\"col_heading level0 col4\" >VADER_ChatGPT_score</th>\n",
              "      <th id=\"T_b0bc2_level0_col5\" class=\"col_heading level0 col5\" >VADER_review_sentiment</th>\n",
              "      <th id=\"T_b0bc2_level0_col6\" class=\"col_heading level0 col6\" >VADER_review_score</th>\n",
              "      <th id=\"T_b0bc2_level0_col7\" class=\"col_heading level0 col7\" >GPT_Error_Check</th>\n",
              "      <th id=\"T_b0bc2_level0_col8\" class=\"col_heading level0 col8\" >VADER_Error_Check</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >ID</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "      <th class=\"blank col6\" >&nbsp;</th>\n",
              "      <th class=\"blank col7\" >&nbsp;</th>\n",
              "      <th class=\"blank col8\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_b0bc2_level0_row0\" class=\"row_heading level0 row0\" >8WNFN9AE</th>\n",
              "      <td id=\"T_b0bc2_row0_col0\" class=\"data row0 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_b0bc2_row0_col1\" class=\"data row0 col1\" >negative</td>\n",
              "      <td id=\"T_b0bc2_row0_col2\" class=\"data row0 col2\" >negative</td>\n",
              "      <td id=\"T_b0bc2_row0_col3\" class=\"data row0 col3\" >negative</td>\n",
              "      <td id=\"T_b0bc2_row0_col4\" class=\"data row0 col4\" >-0.971300</td>\n",
              "      <td id=\"T_b0bc2_row0_col5\" class=\"data row0 col5\" >positive</td>\n",
              "      <td id=\"T_b0bc2_row0_col6\" class=\"data row0 col6\" >0.869600</td>\n",
              "      <td id=\"T_b0bc2_row0_col7\" class=\"data row0 col7\" >Correct</td>\n",
              "      <td id=\"T_b0bc2_row0_col8\" class=\"data row0 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b0bc2_level0_row1\" class=\"row_heading level0 row1\" >5YA546KU</th>\n",
              "      <td id=\"T_b0bc2_row1_col0\" class=\"data row1 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_b0bc2_row1_col1\" class=\"data row1 col1\" >mixed</td>\n",
              "      <td id=\"T_b0bc2_row1_col2\" class=\"data row1 col2\" >positive</td>\n",
              "      <td id=\"T_b0bc2_row1_col3\" class=\"data row1 col3\" >negative</td>\n",
              "      <td id=\"T_b0bc2_row1_col4\" class=\"data row1 col4\" >-0.880700</td>\n",
              "      <td id=\"T_b0bc2_row1_col5\" class=\"data row1 col5\" >positive</td>\n",
              "      <td id=\"T_b0bc2_row1_col6\" class=\"data row1 col6\" >0.666600</td>\n",
              "      <td id=\"T_b0bc2_row1_col7\" class=\"data row1 col7\" >Falsely Negative</td>\n",
              "      <td id=\"T_b0bc2_row1_col8\" class=\"data row1 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b0bc2_level0_row2\" class=\"row_heading level0 row2\" >9W4Z3TFU</th>\n",
              "      <td id=\"T_b0bc2_row2_col0\" class=\"data row2 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_b0bc2_row2_col1\" class=\"data row2 col1\" >mixed</td>\n",
              "      <td id=\"T_b0bc2_row2_col2\" class=\"data row2 col2\" >positive</td>\n",
              "      <td id=\"T_b0bc2_row2_col3\" class=\"data row2 col3\" >positive</td>\n",
              "      <td id=\"T_b0bc2_row2_col4\" class=\"data row2 col4\" >0.928700</td>\n",
              "      <td id=\"T_b0bc2_row2_col5\" class=\"data row2 col5\" >negative</td>\n",
              "      <td id=\"T_b0bc2_row2_col6\" class=\"data row2 col6\" >-0.995600</td>\n",
              "      <td id=\"T_b0bc2_row2_col7\" class=\"data row2 col7\" >Correct</td>\n",
              "      <td id=\"T_b0bc2_row2_col8\" class=\"data row2 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b0bc2_level0_row3\" class=\"row_heading level0 row3\" >K7EEHSC6</th>\n",
              "      <td id=\"T_b0bc2_row3_col0\" class=\"data row3 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_b0bc2_row3_col1\" class=\"data row3 col1\" >mixed</td>\n",
              "      <td id=\"T_b0bc2_row3_col2\" class=\"data row3 col2\" >positive</td>\n",
              "      <td id=\"T_b0bc2_row3_col3\" class=\"data row3 col3\" >positive</td>\n",
              "      <td id=\"T_b0bc2_row3_col4\" class=\"data row3 col4\" >0.931300</td>\n",
              "      <td id=\"T_b0bc2_row3_col5\" class=\"data row3 col5\" >negative</td>\n",
              "      <td id=\"T_b0bc2_row3_col6\" class=\"data row3 col6\" >-0.891600</td>\n",
              "      <td id=\"T_b0bc2_row3_col7\" class=\"data row3 col7\" >Correct</td>\n",
              "      <td id=\"T_b0bc2_row3_col8\" class=\"data row3 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b0bc2_level0_row4\" class=\"row_heading level0 row4\" >CEHEIWR7</th>\n",
              "      <td id=\"T_b0bc2_row4_col0\" class=\"data row4 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_b0bc2_row4_col1\" class=\"data row4 col1\" >mixed</td>\n",
              "      <td id=\"T_b0bc2_row4_col2\" class=\"data row4 col2\" >positive</td>\n",
              "      <td id=\"T_b0bc2_row4_col3\" class=\"data row4 col3\" >negative</td>\n",
              "      <td id=\"T_b0bc2_row4_col4\" class=\"data row4 col4\" >-0.911800</td>\n",
              "      <td id=\"T_b0bc2_row4_col5\" class=\"data row4 col5\" >positive</td>\n",
              "      <td id=\"T_b0bc2_row4_col6\" class=\"data row4 col6\" >0.578900</td>\n",
              "      <td id=\"T_b0bc2_row4_col7\" class=\"data row4 col7\" >Falsely Negative</td>\n",
              "      <td id=\"T_b0bc2_row4_col8\" class=\"data row4 col8\" >Correct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b0bc2_level0_row5\" class=\"row_heading level0 row5\" >2SQ2YDPA</th>\n",
              "      <td id=\"T_b0bc2_row5_col0\" class=\"data row5 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_b0bc2_row5_col1\" class=\"data row5 col1\" >mixed</td>\n",
              "      <td id=\"T_b0bc2_row5_col2\" class=\"data row5 col2\" >positive</td>\n",
              "      <td id=\"T_b0bc2_row5_col3\" class=\"data row5 col3\" >positive</td>\n",
              "      <td id=\"T_b0bc2_row5_col4\" class=\"data row5 col4\" >0.739400</td>\n",
              "      <td id=\"T_b0bc2_row5_col5\" class=\"data row5 col5\" >negative</td>\n",
              "      <td id=\"T_b0bc2_row5_col6\" class=\"data row5 col6\" >-0.980200</td>\n",
              "      <td id=\"T_b0bc2_row5_col7\" class=\"data row5 col7\" >Correct</td>\n",
              "      <td id=\"T_b0bc2_row5_col8\" class=\"data row5 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b0bc2_level0_row6\" class=\"row_heading level0 row6\" >KWC9KUTP</th>\n",
              "      <td id=\"T_b0bc2_row6_col0\" class=\"data row6 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_b0bc2_row6_col1\" class=\"data row6 col1\" >negative</td>\n",
              "      <td id=\"T_b0bc2_row6_col2\" class=\"data row6 col2\" >negative</td>\n",
              "      <td id=\"T_b0bc2_row6_col3\" class=\"data row6 col3\" >negative</td>\n",
              "      <td id=\"T_b0bc2_row6_col4\" class=\"data row6 col4\" >-0.755500</td>\n",
              "      <td id=\"T_b0bc2_row6_col5\" class=\"data row6 col5\" >positive</td>\n",
              "      <td id=\"T_b0bc2_row6_col6\" class=\"data row6 col6\" >0.994100</td>\n",
              "      <td id=\"T_b0bc2_row6_col7\" class=\"data row6 col7\" >Correct</td>\n",
              "      <td id=\"T_b0bc2_row6_col8\" class=\"data row6 col8\" >Falsely Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b0bc2_level0_row7\" class=\"row_heading level0 row7\" >L7JBS7LN</th>\n",
              "      <td id=\"T_b0bc2_row7_col0\" class=\"data row7 col0\" >Nosferatu</td>\n",
              "      <td id=\"T_b0bc2_row7_col1\" class=\"data row7 col1\" >positive</td>\n",
              "      <td id=\"T_b0bc2_row7_col2\" class=\"data row7 col2\" >positive</td>\n",
              "      <td id=\"T_b0bc2_row7_col3\" class=\"data row7 col3\" >positive</td>\n",
              "      <td id=\"T_b0bc2_row7_col4\" class=\"data row7 col4\" >0.943300</td>\n",
              "      <td id=\"T_b0bc2_row7_col5\" class=\"data row7 col5\" >negative</td>\n",
              "      <td id=\"T_b0bc2_row7_col6\" class=\"data row7 col6\" >-0.969200</td>\n",
              "      <td id=\"T_b0bc2_row7_col7\" class=\"data row7 col7\" >Correct</td>\n",
              "      <td id=\"T_b0bc2_row7_col8\" class=\"data row7 col8\" >Falsely Negative</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Group by type of error"
      ],
      "metadata": {
        "id": "LZGXSPKLDxB-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Count the values for each of the error check columns, turn into a transposed dataframe so \"Correct\", \"Falsely Positive\" and \"Falsely Negative\" will be columns\n",
        "VADER_error_check_counts = pd.DataFrame(sentiment[['VADER_Error_Check']].value_counts())\n",
        "ChatGPT_error_check_counts = pd.DataFrame(sentiment[['GPT_Error_Check']].value_counts())\n",
        "\n",
        "# Join the dataframes\n",
        "error_check_counts = pd.concat([VADER_error_check_counts, ChatGPT_error_check_counts], axis=1)\n",
        "\n",
        "# Change column names\n",
        "error_check_counts.columns = [\"VADER analysis of reviews\", \"VADER analysis of ChatGPT outputs\"]\n",
        "\n",
        "# Fix messed up indexes\n",
        "error_check_counts = error_check_counts.reset_index()\n",
        "error_check_counts = error_check_counts.set_index('level_0')\n",
        "error_check_counts.index.name = 'Error Check'"
      ],
      "metadata": {
        "id": "VX485mu3ncQ9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "error_check_counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "fahWU6wpordy",
        "outputId": "6ab5d88a-a367-4f2d-d8a8-97e7c4380f0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  VADER analysis of reviews  VADER analysis of ChatGPT outputs\n",
              "Error Check                                                                   \n",
              "Correct                                  53                                 71\n",
              "Falsely Positive                         17                                  5\n",
              "Falsely Negative                         10                                  4"
            ],
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              "      <th>VADER analysis of reviews</th>\n",
              "      <th>VADER analysis of ChatGPT outputs</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Error Check</th>\n",
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              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Correct</th>\n",
              "      <td>53</td>\n",
              "      <td>71</td>\n",
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              "    <tr>\n",
              "      <th>Falsely Positive</th>\n",
              "      <td>17</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>Falsely Negative</th>\n",
              "      <td>10</td>\n",
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              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-3f8d78a1-c157-4e48-a5c1-2db48304fd70\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3f8d78a1-c157-4e48-a5c1-2db48304fd70')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3f8d78a1-c157-4e48-a5c1-2db48304fd70 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data\n",
        "categories = error_check_counts.index\n",
        "chatgpt_data = error_check_counts[\"VADER analysis of ChatGPT outputs\"]\n",
        "reviews_data = error_check_counts[\"VADER analysis of reviews\"]\n",
        "\n",
        "# Reverse the data and categories\n",
        "categories = categories[::-1]\n",
        "chatgpt_data = chatgpt_data[::-1]\n",
        "reviews_data = reviews_data[::-1]\n",
        "\n",
        "# Set the width of the bars\n",
        "bar_width = 0.40\n",
        "\n",
        "# Set the position of the bars on the y-axis\n",
        "y = np.arange(len(categories))\n",
        "\n",
        "# Set the figure size (width, height)\n",
        "fig, ax = plt.subplots(figsize=(8, 4))\n",
        "\n",
        "# Create the grouped horizontal bar chart\n",
        "bar1 = ax.barh(y + bar_width/2, chatgpt_data, bar_width, label='VADER-ChatGPT')\n",
        "bar2 = ax.barh(y - bar_width/2, reviews_data, bar_width, label='VADER only')\n",
        "\n",
        "# Add labels, title, and legend\n",
        "ax.set_xlabel('Number of Reviews')\n",
        "ax.set_ylabel('Sentiment Categories')\n",
        "ax.set_title('Comparison of Automated Sentiment Analysis Methods')\n",
        "ax.set_yticks(y)\n",
        "ax.set_yticklabels(categories)\n",
        "ax.legend()\n",
        "\n",
        "# Add the exact numbers at the end of each bar\n",
        "for i, val in enumerate(reviews_data):\n",
        "    ax.text(val + 1, i - bar_width/2, str(val), ha='left', va='center')\n",
        "\n",
        "for i, val in enumerate(chatgpt_data):\n",
        "    ax.text(val + 1, i + bar_width/2, str(val), ha='left', va='center')\n",
        "\n",
        "# Adjust the plot layout to accommodate the labels\n",
        "plt.tight_layout()\n",
        "\n",
        "# Display the chart\n",
        "plt.savefig(\"ChatGPT_vs_NLTK_TypeErrors_horizontal_reversed.jpg\", dpi=250, bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "AHdXmsiywodv",
        "outputId": "0e8f2d13-7c99-4ecf-c1b5-f478bdfb1f87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Statements about the scores"
      ],
      "metadata": {
        "id": "BZfuv9nQj7P7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's look at the VADER-only scores\n",
        "df['VADER_review_score'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xO989BMYnORt",
        "outputId": "15b4e2e0-457b-45e1-ede5-6e5626a9ce43"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    80.000000\n",
              "mean      0.599891\n",
              "std       0.690377\n",
              "min      -0.995600\n",
              "25%       0.585050\n",
              "50%       0.969800\n",
              "75%       0.994175\n",
              "max       0.999500\n",
              "Name: VADER_review_score, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's look at the ChatGPT-VADER scores\n",
        "df['VADER_ChatGPT_score'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "btbE12linWY1",
        "outputId": "90ab5958-288b-4369-8e1d-24d8874bba6d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    80.000000\n",
              "mean      0.455839\n",
              "std       0.753081\n",
              "min      -0.971300\n",
              "25%      -0.082775\n",
              "50%       0.924000\n",
              "75%       0.973275\n",
              "max       0.994300\n",
              "Name: VADER_ChatGPT_score, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot distribution of sentiment scores\n",
        "plt.hist(df['VADER_ChatGPT_score'], bins=40, alpha=0.5, label='VADER-ChatGPT Score', color=\"blue\")\n",
        "plt.hist(df['VADER_review_score'], bins=40, alpha=0.5, label='VADER-only Score', color=\"orange\")\n",
        "\n",
        "plt.legend()\n",
        "plt.xlabel('Sentiment Scores')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of Sentiment Scores')\n",
        "plt.show()\n",
        "\n",
        "# Save the chart\n",
        "plt.savefig(\"Histogram_sentiment_values.jpg\", dpi=250, bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "rJkbxjnIpS7m",
        "outputId": "92b3bb5d-1fd9-41c7-e02f-3d16bb8b27b8"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Some Statements"
      ],
      "metadata": {
        "id": "iuKyGM3x5EWk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Statements about ChatGPT-VADER's correctness\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "J0hRVMjQ1kJh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get number of errors that were falsely positive from ChatGPT-VADER\n",
        "false_positives_GPT = sentiment[(sentiment['GPT_Error_Check'] == \"Falsely Positive\")]\n",
        "false_negatives_GPT = sentiment[(sentiment['GPT_Error_Check'] == \"Falsely Negative\")]\n",
        "correct_GPT = sentiment[(sentiment['GPT_Error_Check'] == \"Correct\")]\n",
        "\n",
        "# Print statements\n",
        "print(f\"There are \\033[1m{len(false_positives_GPT)}\\033[0m cases in which the ChatGPT-VADER judgment was falsely positive, that is, \\033[1m{len(false_positives_GPT)/len(sentiment)*100}%\\033[0m of the time.\")\n",
        "print(f\"There are \\033[1m{len(false_negatives_GPT)}\\033[0m cases in which the ChatGPT-VADER judgment was falsely negative, that is, \\033[1m{len(false_negatives_GPT)/len(sentiment)*100}%\\033[0m of the time.\")\n",
        "print(f\"There are \\033[1m{len(correct_GPT)}\\033[0m cases in which the direct ChatGPT-VADER judgment was correct, that is, \\033[1m{len(correct_GPT)/len(sentiment)*100}%\\033[0m of the time.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vPryr40fzrz2",
        "outputId": "9ba5a7f7-d478-43c9-83ab-d70def444e24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are \u001b[1m5\u001b[0m cases in which the ChatGPT-VADER judgment was falsely positive, that is, \u001b[1m6.25%\u001b[0m of the time.\n",
            "There are \u001b[1m4\u001b[0m cases in which the ChatGPT-VADER judgment was falsely negative, that is, \u001b[1m5.0%\u001b[0m of the time.\n",
            "There are \u001b[1m71\u001b[0m cases in which the direct ChatGPT-VADER judgment was correct, that is, \u001b[1m88.75%\u001b[0m of the time.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Statements about direct VADER's correctness"
      ],
      "metadata": {
        "id": "pBbvU4qZ1sOa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get number of errors that were falsely positive from VADER directly\n",
        "false_positives_VADER = sentiment[(sentiment['VADER_Error_Check'] == \"Falsely Positive\")]\n",
        "false_negatives_VADER = sentiment[(sentiment['VADER_Error_Check'] == \"Falsely Negative\")]\n",
        "correct_VADER = sentiment[(sentiment['VADER_Error_Check'] == \"Correct\")]\n",
        "\n",
        "# Print statements\n",
        "print(f\"There are \\033[1m{len(false_positives_VADER)}\\033[0m cases in which the direct VADER judgment was falsely positive, that is, \\033[1m{len(false_positives_VADER)/len(sentiment)*100}%\\033[0m of the time.\")\n",
        "print(f\"There are \\033[1m{len(false_negatives_VADER)}\\033[0m cases in which the direct VADER judgment was falsely negative, that is, \\033[1m{len(false_negatives_VADER)/len(sentiment)*100}%\\033[0m of the time.\")\n",
        "print(f\"There are \\033[1m{len(correct_VADER)}\\033[0m cases in which the direct VADER judgment was correct, that is, \\033[1m{len(correct_VADER)/len(sentiment)*100}%\\033[0m of the time.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ab263422-d70b-4561-e14d-cefcf622427f",
        "id": "pZLel3BS0XeR"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are \u001b[1m17\u001b[0m cases in which the direct VADER judgment was falsely positive, that is, \u001b[1m21.25%\u001b[0m of the time.\n",
            "There are \u001b[1m10\u001b[0m cases in which the direct VADER judgment was falsely negative, that is, \u001b[1m12.5%\u001b[0m of the time.\n",
            "There are \u001b[1m53\u001b[0m cases in which the direct VADER judgment was correct, that is, \u001b[1m66.25%\u001b[0m of the time.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Errors per movie"
      ],
      "metadata": {
        "id": "mQZqv87s2Xso"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Caligari"
      ],
      "metadata": {
        "id": "osvZcvFN7BEj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get sentiments for Caligari\n",
        "caligari_sentiment =  sentiment[(sentiment['Movie'] == 'Caligari')]\n",
        "\n",
        "# Get number of errors for Caligari\n",
        "caligari_errors = caligari_sentiment[(caligari_sentiment['GPT_Error_Check'] != 'Correct') | (caligari_sentiment['VADER_Error_Check'] != 'Correct')]\n",
        "\n",
        "VADER_errors_caligari = caligari_sentiment[(caligari_sentiment['VADER_Error_Check'] != 'Correct')]\n",
        "VADER_falsely_positive_Caligari = caligari_sentiment[(caligari_sentiment['VADER_Error_Check'] == 'Falsely Positive')]\n",
        "VADER_falsely_negative_Caligari = caligari_sentiment[(caligari_sentiment['VADER_Error_Check'] == 'Falsely Negative')]\n",
        "\n",
        "ChatGPT_errors_caligari = caligari_sentiment[(caligari_sentiment['GPT_Error_Check'] != 'Correct')]\n",
        "ChatGPT_falsely_positive_Caligari = caligari_sentiment[(caligari_sentiment['GPT_Error_Check'] == 'Falsely Positive')]\n",
        "ChatGPT_wrongly_negative_Caligari = caligari_sentiment[(caligari_sentiment['GPT_Error_Check'] == 'Falsely Negative')]"
      ],
      "metadata": {
        "id": "co-mvKR12axb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Overall mistakes for Caligari\n",
        "print(f\"There are \\033[1m{len(caligari_errors)}\\033[0m cases in which at least one of the automated methods got the sentiment of Caligari reviews wrong, that is, \\033[1m{len(caligari_errors)/len(caligari_sentiment)*100}%\\033[0m of the reviews of the film.\")\n",
        "\n",
        "# VADER mistakes for Caligari\n",
        "print(\"\\n\")\n",
        "print(f\"Out of those, the \\033[1mdirect VADER model\\033[0m made mistakes in \\033[1m{len(VADER_errors_caligari)}\\033[0m reviews of Caligari.\")\n",
        "print(f\"The \\033[1mdirect VADER model\\033[0m wrongly characterized \\033[1m{len(VADER_falsely_positive_Caligari)}\\033[0m reviews of Caligari as \\033[1mpositive\\033[0m.\")\n",
        "print(f\"The \\033[1mdirect VADER model\\033[0m wrongly characterized \\033[1m{len(VADER_falsely_negative_Caligari)}\\033[0m reviews of Caligari as \\033[1mnegative\\033[0m.\")\n",
        "\n",
        "# ChatGPT mistakes for Caligari\n",
        "print(\"\\n\")\n",
        "print(f\"The \\033[1mChatGPT-VADER model\\033[0m wrongly characterized \\033[1m{len(ChatGPT_errors_caligari)}\\033[0m reviews of Caligari\")\n",
        "print(f\"The \\033[1mChatGPT-VADER model\\033[0m wrongly characterized \\033[1m{len(ChatGPT_falsely_positive_Caligari)}\\033[0m reviews of Caligari as \\033[1mpositive\\033[0m.\")\n",
        "print(f\"The \\033[1mChatGPT-VADER model\\033[0m wrongly characterized \\033[1m{len(ChatGPT_wrongly_negative_Caligari)}\\033[0m reviews of Caligari as \\033[1mnegative\\033[0m.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BPYVA5uI7byo",
        "outputId": "94aaacec-a202-4765-82e0-e78be3a02969"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are \u001b[1m10\u001b[0m cases in which at least one of the automated methods got the sentiment of Caligari reviews wrong, that is, \u001b[1m26.31578947368421%\u001b[0m of the reviews of the film.\n",
            "\n",
            "\n",
            "Out of those, the \u001b[1mdirect VADER model\u001b[0m made mistakes in \u001b[1m10\u001b[0m reviews of Caligari.\n",
            "The \u001b[1mdirect VADER model\u001b[0m wrongly characterized \u001b[1m4\u001b[0m reviews of Caligari as \u001b[1mpositive\u001b[0m.\n",
            "The \u001b[1mdirect VADER model\u001b[0m wrongly characterized \u001b[1m6\u001b[0m reviews of Caligari as \u001b[1mnegative\u001b[0m.\n",
            "\n",
            "\n",
            "The \u001b[1mChatGPT-VADER model\u001b[0m wrongly characterized \u001b[1m2\u001b[0m reviews of Caligari\n",
            "The \u001b[1mChatGPT-VADER model\u001b[0m wrongly characterized \u001b[1m1\u001b[0m reviews of Caligari as \u001b[1mpositive\u001b[0m.\n",
            "The \u001b[1mChatGPT-VADER model\u001b[0m wrongly characterized \u001b[1m1\u001b[0m reviews of Caligari as \u001b[1mnegative\u001b[0m.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Metropolis"
      ],
      "metadata": {
        "id": "ozRGt1Wq8lV3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get sentiments for metropolis\n",
        "metropolis_sentiment =  sentiment[(sentiment['Movie'] == 'Metropolis')]\n",
        "\n",
        "# Get number of errors for metropolis\n",
        "metropolis_errors = metropolis_sentiment[(metropolis_sentiment['GPT_Error_Check'] != 'Correct') | (metropolis_sentiment['VADER_Error_Check'] != 'Correct')]\n",
        "\n",
        "VADER_errors_metropolis = metropolis_sentiment[(metropolis_sentiment['VADER_Error_Check'] != 'Correct')]\n",
        "VADER_falsely_positive_metropolis = metropolis_sentiment[(metropolis_sentiment['VADER_Error_Check'] == 'Falsely Positive')]\n",
        "VADER_falsely_negative_metropolis = metropolis_sentiment[(metropolis_sentiment['VADER_Error_Check'] == 'Falsely Negative')]\n",
        "\n",
        "ChatGPT_errors_metropolis = metropolis_sentiment[(metropolis_sentiment['GPT_Error_Check'] != 'Correct')]\n",
        "ChatGPT_falsely_positive_metropolis = metropolis_sentiment[(metropolis_sentiment['GPT_Error_Check'] == 'Falsely Positive')]\n",
        "ChatGPT_wrongly_negative_metropolis = metropolis_sentiment[(metropolis_sentiment['GPT_Error_Check'] == 'Falsely Negative')]"
      ],
      "metadata": {
        "id": "T5BDfADS_cBx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Overall mistakes for metropolis\n",
        "print(f\"There are \\033[1m{len(metropolis_errors)}\\033[0m cases in which at least one of the automated methods got the sentiment of Metropolis reviews wrong, that is, \\033[1m{len(metropolis_errors)/len(metropolis_sentiment)*100}%\\033[0m of the reviews of the film.\")\n",
        "\n",
        "# VADER mistakes for metropolis\n",
        "print(\"\\n\")\n",
        "print(f\"Out of those, the \\033[1mdirect VADER model\\033[0m made mistakes in \\033[1m{len(VADER_errors_metropolis)}\\033[0m reviews of Metropolis.\")\n",
        "print(f\"The \\033[1mdirect VADER model\\033[0m wrongly characterized \\033[1m{len(VADER_falsely_positive_metropolis)}\\033[0m reviews of Metropolis as \\033[1mpositive\\033[0m.\")\n",
        "print(f\"The \\033[1mdirect VADER model\\033[0m wrongly characterized \\033[1m{len(VADER_falsely_negative_metropolis)}\\033[0m reviews of Metropolis as \\033[1mnegative\\033[0m.\")\n",
        "\n",
        "# ChatGPT mistakes for metropolis\n",
        "print(\"\\n\")\n",
        "print(f\"The \\033[1mChatGPT-VADER model\\033[0m wrongly characterized \\033[1m{len(ChatGPT_errors_metropolis)}\\033[0m reviews of Metropolis.\")\n",
        "print(f\"The \\033[1mChatGPT-VADER model\\033[0m wrongly characterized \\033[1m{len(ChatGPT_falsely_positive_metropolis)}\\033[0m reviews of Metropolis as \\033[1mpositive\\033[0m.\")\n",
        "print(f\"The \\033[1mChatGPT-VADER model\\033[0m wrongly characterized \\033[1m{len(ChatGPT_wrongly_negative_metropolis)}\\033[0m reviews of Metropolis as \\033[1mnegative\\033[0m.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8cbb71d-cf7c-4876-ed42-f93c91b1e0df",
        "id": "nwfCIYr6_cBy"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are \u001b[1m12\u001b[0m cases in which at least one of the automated methods got the sentiment of Metropolis reviews wrong, that is, \u001b[1m48.0%\u001b[0m of the reviews of the film.\n",
            "\n",
            "\n",
            "Out of those, the \u001b[1mdirect VADER model\u001b[0m made mistakes in \u001b[1m11\u001b[0m reviews of Metropolis.\n",
            "The \u001b[1mdirect VADER model\u001b[0m wrongly characterized \u001b[1m11\u001b[0m reviews of Metropolis as \u001b[1mpositive\u001b[0m.\n",
            "The \u001b[1mdirect VADER model\u001b[0m wrongly characterized \u001b[1m0\u001b[0m reviews of Metropolis as \u001b[1mnegative\u001b[0m.\n",
            "\n",
            "\n",
            "The \u001b[1mChatGPT-VADER model\u001b[0m wrongly characterized \u001b[1m5\u001b[0m reviews of Metropolis.\n",
            "The \u001b[1mChatGPT-VADER model\u001b[0m wrongly characterized \u001b[1m4\u001b[0m reviews of Metropolis as \u001b[1mpositive\u001b[0m.\n",
            "The \u001b[1mChatGPT-VADER model\u001b[0m wrongly characterized \u001b[1m1\u001b[0m reviews of Metropolis as \u001b[1mnegative\u001b[0m.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Nosferatu"
      ],
      "metadata": {
        "id": "AZhPe_OwAPnt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get sentiments for nosferatu\n",
        "nosferatu_sentiment =  sentiment[(sentiment['Movie'] == 'Nosferatu')]\n",
        "\n",
        "# Get number of errors for nosferatu\n",
        "nosferatu_errors = nosferatu_sentiment[(nosferatu_sentiment['GPT_Error_Check'] != 'Correct') | (nosferatu_sentiment['VADER_Error_Check'] != 'Correct')]\n",
        "\n",
        "VADER_errors_nosferatu = nosferatu_sentiment[(nosferatu_sentiment['VADER_Error_Check'] != 'Correct')]\n",
        "VADER_falsely_positive_nosferatu = nosferatu_sentiment[(nosferatu_sentiment['VADER_Error_Check'] == 'Falsely Positive')]\n",
        "VADER_falsely_negative_nosferatu = nosferatu_sentiment[(nosferatu_sentiment['VADER_Error_Check'] == 'Falsely Negative')]\n",
        "\n",
        "ChatGPT_errors_nosferatu = nosferatu_sentiment[(nosferatu_sentiment['GPT_Error_Check'] != 'Correct')]\n",
        "ChatGPT_falsely_positive_nosferatu = nosferatu_sentiment[(nosferatu_sentiment['GPT_Error_Check'] == 'Falsely Positive')]\n",
        "ChatGPT_wrongly_negative_nosferatu = nosferatu_sentiment[(nosferatu_sentiment['GPT_Error_Check'] == 'Falsely Negative')]"
      ],
      "metadata": {
        "id": "H7pqd-9yAPnt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Overall mistakes for nosferatu\n",
        "print(f\"There are \\033[1m{len(nosferatu_errors)}\\033[0m cases in which at least one of the automated methods got the sentiment of Nosferatu reviews wrong, that is, \\033[1m{len(nosferatu_errors)/len(nosferatu_sentiment)*100}%\\033[0m of the reviews of the film.\")\n",
        "\n",
        "# VADER mistakes for nosferatu\n",
        "print(\"\\n\")\n",
        "print(f\"Out of those, the \\033[1mdirect VADER model\\033[0m made mistakes in \\033[1m{len(VADER_errors_nosferatu)}\\033[0m reviews of Nosferatu.\")\n",
        "print(f\"The \\033[1mdirect VADER model\\033[0m wrongly characterized \\033[1m{len(VADER_falsely_positive_nosferatu)}\\033[0m reviews of Nosferatu as \\033[1mpositive\\033[0m.\")\n",
        "print(f\"The \\033[1mdirect VADER model\\033[0m wrongly characterized \\033[1m{len(VADER_falsely_negative_nosferatu)}\\033[0m reviews of Nosferatu as \\033[1mnegative\\033[0m.\")\n",
        "\n",
        "# ChatGPT mistakes for nosferatu\n",
        "print(\"\\n\")\n",
        "print(f\"The \\033[1mChatGPT-VADER model\\033[0m wrongly characterized \\033[1m{len(ChatGPT_errors_nosferatu)}\\033[0m reviews of Nosferatu.\")\n",
        "print(f\"The \\033[1mChatGPT-VADER model\\033[0m wrongly characterized \\033[1m{len(ChatGPT_falsely_positive_nosferatu)}\\033[0m reviews of Nosferatu as \\033[1mpositive\\033[0m.\")\n",
        "print(f\"The \\033[1mChatGPT-VADER model\\033[0m wrongly characterized \\033[1m{len(ChatGPT_wrongly_negative_nosferatu)}\\033[0m reviews of Nosferatu as \\033[1mnegative\\033[0m.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ddddfb5-8081-4722-c09f-a8cb7311308c",
        "id": "IO211rq0APnu"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are \u001b[1m8\u001b[0m cases in which at least one of the automated methods got the sentiment of Nosferatu reviews wrong, that is, \u001b[1m47.05882352941176%\u001b[0m of the reviews of the film.\n",
            "\n",
            "\n",
            "Out of those, the \u001b[1mdirect VADER model\u001b[0m made mistakes in \u001b[1m6\u001b[0m reviews of Nosferatu.\n",
            "The \u001b[1mdirect VADER model\u001b[0m wrongly characterized \u001b[1m2\u001b[0m reviews of Nosferatu as \u001b[1mpositive\u001b[0m.\n",
            "The \u001b[1mdirect VADER model\u001b[0m wrongly characterized \u001b[1m4\u001b[0m reviews of Nosferatu as \u001b[1mnegative\u001b[0m.\n",
            "\n",
            "\n",
            "The \u001b[1mChatGPT-VADER model\u001b[0m wrongly characterized \u001b[1m2\u001b[0m reviews of Nosferatu.\n",
            "The \u001b[1mChatGPT-VADER model\u001b[0m wrongly characterized \u001b[1m0\u001b[0m reviews of Nosferatu as \u001b[1mpositive\u001b[0m.\n",
            "The \u001b[1mChatGPT-VADER model\u001b[0m wrongly characterized \u001b[1m2\u001b[0m reviews of Nosferatu as \u001b[1mnegative\u001b[0m.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Errors overall"
      ],
      "metadata": {
        "id": "Kc8TnAhtOMf5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\t# Get the count of mistakes from the indirect GPT sentiment analysis\n",
        "false_GPT = divergent[(divergent['GPT_Error_Check'] != \"Correct\")]\n",
        "false_GPT_alone = false_GPT[(false_GPT['VADER_Error_Check'] == \"Correct\")]\n",
        "\n",
        "# Get the count of mistakes from the direct NLTK sentiment analysis\n",
        "false_VADER = divergent[(divergent['VADER_Error_Check'] != \"Correct\")]\n",
        "false_VADER_alone = false_VADER[(false_VADER['GPT_Error_Check'] == \"Correct\")]\n",
        "\n",
        "# Get the count of mistakes that they made together\n",
        "false_together = divergent[(divergent['GPT_Error_Check'] != \"Correct\") & (divergent['VADER_Error_Check'] != \"Correct\")]\n",
        "\n",
        "# Print statements\n",
        "print(f\"There are \\033[1m{len(divergent)}\\033[0m rows in which one of the machine sentiments diverged from the human annotated sentiment, that is, \\033[1m{len(divergent)/len(sentiment)*100}%\\033[0m of the time.\")\n",
        "print(f\"The direct NLTK sentiment analysis diverged from the human sentiment \\033[1m{len(false_VADER)}\\033[0m times, that is, \\033[1m{len(false_VADER)/len(sentiment)*100}%\\033[0m of the time. Out of those, it erred alone \\033[1m{len(false_VADER_alone)}\\033[0m times.\")\n",
        "print(f\"The indirect NLTK sentiment analysis by way of ChatGPT diverged from the human sentiment \\033[1m{len(false_GPT)}\\033[0m times, that is, \\033[1m{len(false_GPT)/len(sentiment)*100}%\\033[0m of the time. Out of those, it erred alone \\033[1m{len(false_GPT_alone)}\\033[0m times.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PkA2QN2TOVc4",
        "outputId": "8addaedd-262a-406e-c4cf-199a29c6464a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are \u001b[1m30\u001b[0m rows in which one of the machine sentiments diverged from the human annotated sentiment, that is, \u001b[1m37.5%\u001b[0m of the time.\n",
            "The direct NLTK sentiment analysis diverged from the human sentiment \u001b[1m27\u001b[0m times, that is, \u001b[1m33.75%\u001b[0m of the time. Out of those, it erred alone \u001b[1m21\u001b[0m times.\n",
            "The indirect NLTK sentiment analysis by way of ChatGPT diverged from the human sentiment \u001b[1m9\u001b[0m times, that is, \u001b[1m11.25%\u001b[0m of the time. Out of those, it erred alone \u001b[1m3\u001b[0m times.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#"
      ],
      "metadata": {
        "id": "xOYK4iBqj15N"
      }
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}